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Alessandro L, Crema S, Castiglione JI, Dossi D, Eberbach F, Kohler A, Laffue A, Marone A, Nagel V, Pastor Rueda JM, Varela F, Fernandez Slezak D, Rodríguez Murúa S, Debasa C, Claudio P, Farez MF. Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology. Neurologist 2025; 30:155-163. [PMID: 39912331 DOI: 10.1097/nrl.0000000000000594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2025]
Abstract
OBJECTIVES Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety and accuracy. Our objective is to evaluate the diagnostic safety and effectiveness of an AI-powered virtual assistant (VA) designed for the triage of neurological pathologies. METHODS The performance of an AI-powered VA for emergency neurological triage was tested. Ten patients over 18 years old with urgent neurological pathologies were selected. In the first stage, 9 neurologists assessed the safety of the VA using their clinical records. In the second stage, the assistant's accuracy when used by patients was evaluated. Finally, VA performance was compared with ChatGPT 3.5 and 4. RESULTS In stage 1, neurologists agreed with the VA in 98.5% of the cases for syndromic diagnosis, and in all cases, the definitive diagnosis was among the top 5 differentials. In stage 2, neurologists agreed with all diagnostic parameters and recommendations suggested by the assistant to patients. The average use time was 5.5 minutes (average of 16.5 questions). VA showed superiority over both versions of ChatGPT in all evaluated diagnostic and safety aspects ( P <0.0001). In 57.8% of the evaluations, neurologists rated the VA as "excellent" (suggesting adequate utility). CONCLUSIONS In this study, the VA showcased promising diagnostic accuracy and user satisfaction, bolstering confidence in further development. These outcomes encourage proceeding to a comprehensive phase 1/2 trial with 100 patients to thoroughly assess its "real-time" application in emergency neurological triage.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Diego Fernandez Slezak
- Entelai
- Department of Computing, Faculty of Exact and Natural Sciences, University of Buenos Aires (UBA)
- Institute of Research in Computer Science (ICC), CONICET-UBA, Buenos Aires, Argentina
| | | | | | | | - Mauricio F Farez
- Center for Research in Neuroimmunological Diseases (CIEN), Fleni
- Entelai
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Humphreys SC, Block JE, Sivaganesan A, Nel LJ, Peterman M, Hodges SD. Optimizing the clinical adoption of total joint replacement of the lumbar spine through imaging, robotics and artificial intelligence. Expert Rev Med Devices 2025; 22:405-413. [PMID: 40143511 DOI: 10.1080/17434440.2025.2484252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2024] [Accepted: 03/21/2025] [Indexed: 03/28/2025]
Abstract
INTRODUCTION The objective of this article is to assess the potential of imaging, robotics, and artificial intelligence (AI) to significantly improve spine care, preoperative planning and surgery. AREAS COVERED This article describes the development of lumbar total joint replacement (TJR) of the spine (MOTUS, 3Spine, Chattanooga, TN, U.S.A.). We discuss the evolution of intra-operative imaging, robotics, and AI and how these trends can intersect with lumbar TJR to optimize the safety, efficiency, and accessibility of the procedure. EXPERT OPINION By preserving natural spinal motion, TJR represents a significant leap forward in the treatment of degenerative spinal conditions by providing an alternative to fusion. This transformation has already occurred and is continuing to evolve in the primary synovial joints such as hip, knee, shoulder and ankle where arthroplasty outcomes are now so superior that fusion is considered a salvage procedure. The convergence of imaging, robotics and AI is poised to reshape spine care by enhancing precision and safety, personalizing treatment pathways, lowering production costs, and accelerating adoption. However, the key challenges include ensuring continued collaboration between surgeons, researchers, manufacturers, and regulatory bodies to optimize the potential of TJR.
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Affiliation(s)
| | - Jon E Block
- Independent Consultant, San Francisco, CA, USA
| | | | - Louis J Nel
- Neurosurgery, Zuid Afrikaans Hospital, Pretoria, South Africa
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Rincón EHH, Jimenez D, Aguilar LAC, Flórez JMP, Tapia ÁER, Peñuela CLJ. Mapping the use of artificial intelligence in medical education: a scoping review. BMC MEDICAL EDUCATION 2025; 25:526. [PMID: 40221725 PMCID: PMC11993958 DOI: 10.1186/s12909-025-07089-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2024] [Accepted: 04/01/2025] [Indexed: 04/14/2025]
Abstract
INTRODUCTION The integration of artificial intelligence (AI) in healthcare has transformed clinical practices and medical education, with technologies like diagnostic algorithms and clinical decision support increasingly incorporated into curricula. However, there is still a gap in preparing future physicians to use these technologies effectively and ethically. OBJECTIVE This scoping review maps the integration of artificial intelligence (AI) in undergraduate medical education (UME), focusing on curriculum development, student competency enhancement, and institutional barriers to AI adoption. MATERIALS AND METHODS A comprehensive search in PubMed, Scopus, and BIREME included articles from 2019 onwards, limited to English and Spanish publications on AI in UME. Exclusions applied to studies focused on postgraduate education or non-medical fields. Data were analyzed using thematic analysis to identify patterns in AI curriculum development and implementation. RESULTS A total of 34 studies were reviewed, representing diverse regions and methodologies, including cross-sectional studies, narrative reviews, and intervention studies. Findings revealed a lack of standardized AI curriculum frameworks and notable global discrepancies. Key elements such as ethical training, collaborative learning, and digital competence were identified as essential, with an emphasis on transversal skills that support AI as a tool rather than a standalone subject. CONCLUSIONS This review underscores the need for a standardized, adaptable AI curriculum in UME that prioritizes transversal skills, including digital competence and ethical awareness, to support AI's gradual integration. Embedding AI as a practical tool within interdisciplinary, patient-centered frameworks fosters a balanced approach to technology in healthcare. Further regional research is recommended to develop frameworks that align with cultural and educational needs, ensuring AI integration in UME promotes both technical and ethical competencies.
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Affiliation(s)
- Erwin Hernando Hernández Rincón
- Department of Family Medicine and Public Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia.
| | - Daniel Jimenez
- Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
| | - Lizeth Alexandra Chavarro Aguilar
- Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
| | - Juan Miguel Pérez Flórez
- Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
| | - Álvaro Enrique Romero Tapia
- Department of Psychiatry and Mental Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
| | - Claudia Liliana Jaimes Peñuela
- Department of Family Medicine and Public Health, Facultad de Medicina, Universidad de La Sabana, Campus del Puente del Común, Km. 7, Autopista Norte de Bogotá, Chía, Cundinamarca, Colombia
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Ramakrishnaiah Y, Macesic N, Webb GI, Peleg AY, Tyagi S. EHR-ML: A data-driven framework for designing machine learning applications with electronic health records. Int J Med Inform 2025; 196:105816. [PMID: 39891983 DOI: 10.1016/j.ijmedinf.2025.105816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2024] [Revised: 01/22/2025] [Accepted: 01/26/2025] [Indexed: 02/03/2025]
Abstract
OBJECTIVE The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, its integration faces challenges resulting in a crisis of generalisability. Key obstacles include; 1) Insufficient consideration of local contextual factors, such as institution-specific data formats, practices, and protocols, which can lead to variability in clinical practices across different institutions. 2) ad-hoc data preparation and design of machine learning strategies. 3) manual subjective adjustment of design parameters resulting in sub-optimal performance. 4) EHR specific challenges regarding data biases affecting the model outcomes and unique intermittent temporal nature of the data necessitating specialised handling 5) lack of cross-institutional data validations. METHODS To address these challenges, EHR-ML, provides an easy to use structured framework for designing optimum machine learning applications in a data-driven manner. The framework supports ingestion of local institutional electronic health records (EHRs) and process standardisation. The study design and parameter optimisation is done in a fully data-driven evidence-based approach. It seamlessly integrating with existing quality control tools. To handle the unique characteristics of the EHR data, it offers customisable ensemble models. It enables the acquisition of EHR data from diverse systems and harmonise them into common formats following international standards. RESULTS The effectiveness of the EHR-ML is demonstrated through a series of case studies. These studies highlight its capability to develop high-performance models in a fully automated manner, consistently surpassing the performance of traditional methodologies. Furthermore, they exhibited strong generalisability across diverse healthcare settings. DISCUSSION AND CONCLUSION EHR-ML enhances the clinical relevance and accuracy of predictive models by incorporating local context into machine learning applications. Additionally, by providing an user-friendly fully-automated framework, it facilitates rapid hypothesis testing aimed to generate localised biomedical knowledge.
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Affiliation(s)
- Yashpal Ramakrishnaiah
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, 3000, VIC, Australia
| | - Nenad Macesic
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne, 3000, VIC, Australia
| | - Geoffrey I Webb
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne, 3000, VIC, Australia
| | - Anton Y Peleg
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, 3000, VIC, Australia; Centre to Impact AMR, Monash University, Melbourne, 3000, VIC, Australia
| | - Sonika Tyagi
- Department of Infectious Diseases, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, 3000, VIC, Australia; School of Computing Technologies, RMIT University, Melbourne, 3000, VIC, Australia.
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Takita H, Kabata D, Walston SL, Tatekawa H, Saito K, Tsujimoto Y, Miki Y, Ueda D. A systematic review and meta-analysis of diagnostic performance comparison between generative AI and physicians. NPJ Digit Med 2025; 8:175. [PMID: 40121370 PMCID: PMC11929846 DOI: 10.1038/s41746-025-01543-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Accepted: 02/26/2025] [Indexed: 03/25/2025] Open
Abstract
While generative artificial intelligence (AI) has shown potential in medical diagnostics, comprehensive evaluation of its diagnostic performance and comparison with physicians has not been extensively explored. We conducted a systematic review and meta-analysis of studies validating generative AI models for diagnostic tasks published between June 2018 and June 2024. Analysis of 83 studies revealed an overall diagnostic accuracy of 52.1%. No significant performance difference was found between AI models and physicians overall (p = 0.10) or non-expert physicians (p = 0.93). However, AI models performed significantly worse than expert physicians (p = 0.007). Several models demonstrated slightly higher performance compared to non-experts, although the differences were not significant. Generative AI demonstrates promising diagnostic capabilities with accuracy varying by model. Although it has not yet achieved expert-level reliability, these findings suggest potential for enhancing healthcare delivery and medical education when implemented with appropriate understanding of its limitations.
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Affiliation(s)
- Hirotaka Takita
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Daijiro Kabata
- Center for Mathematical and Data Science, Kobe University, Kobe, Japan
| | - Shannon L Walston
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Hiroyuki Tatekawa
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Kenichi Saito
- Center for Digital Transformation of Health Care, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yasushi Tsujimoto
- Oku Medical Clinic, Osaka, Japan
- Department of Health Promotion and Human Behavior, Kyoto University Graduate School of Medicine/School of Public Health, Kyoto University, Kyoto, Japan
- Scientific Research WorkS Peer Support Group (SRWS-PSG), Osaka, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
- Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan.
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Zivarifar H, Ahrari-Roodi T, Keikha M. Commentary on "Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualitative Systematic Review" by Nogueira et al. (2025). Aesthetic Plast Surg 2025:10.1007/s00266-025-04825-9. [PMID: 40105945 DOI: 10.1007/s00266-025-04825-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2025] [Accepted: 02/28/2025] [Indexed: 03/22/2025]
Abstract
No Level Assigned This journal requires that authors assign a level of evidence to each submission to which Evidence-Based Medicine rankings are applicable. This excludes Review Articles, Book Reviews, and manuscripts that concern Basic Science, Animal Studies, Cadaver Studies, and Experimental Studies. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .
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Affiliation(s)
- Hamidreza Zivarifar
- Department of Internal Medicine, and Virology, School of Medicine, Zahedan University of Medical Sciences, Zahedan, Iran
- Clinical Immunology Research Center, Ali-Ebne Abitaleb Hospital, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Tahereh Ahrari-Roodi
- Clinical Immunology Research Center, Ali-Ebne Abitaleb Hospital, Zahedan University of Medical Sciences, Zahedan, Iran
| | - Masoud Keikha
- Department of Medical Microbiology, School of Medicine, Iranshahr University of Medical Sciences, Iranshahr, Iran.
- Tropical and Communicable Diseases Research Center, Iranshahr University of Medical Sciences, Iranshahr, Iran.
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Hu S, Oppong A, Mogo E, Collins C, Occhini G, Barford A, Korhonen A. Natural Language Processing Technologies for Public Health in Africa: Scoping Review. J Med Internet Res 2025; 27:e68720. [PMID: 40053738 PMCID: PMC11923465 DOI: 10.2196/68720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2024] [Revised: 01/09/2025] [Accepted: 01/24/2025] [Indexed: 03/09/2025] Open
Abstract
BACKGROUND Natural language processing (NLP) has the potential to promote public health. However, applying these technologies in African health systems faces challenges, including limited digital and computational resources to support the continent's diverse languages and needs. OBJECTIVE This scoping review maps the evidence on NLP technologies for public health in Africa, addressing the following research questions: (1) What public health needs are being addressed by NLP technologies in Africa, and what unmet needs remain? (2) What factors influence the availability of public health NLP technologies across African countries and languages? (3) What stages of deployment have these technologies reached, and to what extent have they been integrated into health systems? (4) What measurable impact has these technologies had on public health outcomes, where such data are available? (5) What recommendations have been proposed to enhance the quality, cost, and accessibility of health-related NLP technologies in Africa? METHODS This scoping review includes academic studies published between January 1, 2013, and October 3, 2024. A systematic search was conducted across databases, including MEDLINE via PubMed, ACL Anthology, Scopus, IEEE Xplore, and ACM Digital Library, supplemented by gray literature searches. Data were extracted and the NLP technology functions were mapped to the World Health Organization's list of essential public health functions and the United Nations' sustainable development goals (SDGs). The extracted data were analyzed to identify trends, gaps, and areas for future research. This scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) reporting guidelines, and its protocol is publicly available. RESULTS Of 2186 citations screened, 54 studies were included. While existing NLP technologies support a subset of essential public health functions and SDGs, language coverage remains uneven, with limited support for widely spoken African languages, such as Kiswahili, Yoruba, Igbo, and Zulu, and no support for most of Africa's >2000 languages. Most technologies are in prototyping phases, with only one fully deployed chatbot addressing vaccine hesitancy. Evidence of measurable impact is limited, with 15% (8/54) studies attempting health-related evaluations and 4% (2/54) demonstrating positive public health outcomes, including improved participants' mood and increased vaccine intentions. Recommendations include expanding language coverage, targeting local health needs, enhancing trust, integrating solutions into health systems, and adopting participatory design approaches. The gray literature reveals industry- and nongovernmental organizations-led projects focused on deployable NLP applications. However, these projects tend to support only a few major languages and specific use cases, indicating a narrower scope than academic research. CONCLUSIONS Despite growth in NLP research for public health, major gaps remain in deployment, linguistic inclusivity, and health outcome evaluation. Future research should prioritize cross-sectoral and needs-based approaches that engage local communities, align with African health systems, and incorporate rigorous evaluations to enhance public health outcomes. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-doi:10.1101/2024.07.02.24309815.
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Affiliation(s)
- Songbo Hu
- Language Technology Lab, University of Cambridge, Cambridge, United Kingdom
| | - Abigail Oppong
- Language Technology Lab, University of Cambridge, Cambridge, United Kingdom
| | - Ebele Mogo
- Cambridge Centre for Human Inspired Artificial Intelligence, University of Cambridge, Cambridge, United Kingdom
| | - Charlotte Collins
- Language Technology Lab, University of Cambridge, Cambridge, United Kingdom
| | - Giulia Occhini
- Language Technology Lab, University of Cambridge, Cambridge, United Kingdom
| | - Anna Barford
- Language Technology Lab, University of Cambridge, Cambridge, United Kingdom
| | - Anna Korhonen
- Language Technology Lab, University of Cambridge, Cambridge, United Kingdom
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Rao VM, Hla M, Moor M, Adithan S, Kwak S, Topol EJ, Rajpurkar P. Multimodal generative AI for medical image interpretation. Nature 2025; 639:888-896. [PMID: 40140592 DOI: 10.1038/s41586-025-08675-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 01/20/2025] [Indexed: 03/28/2025]
Abstract
Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as multimodal generative medical image interpretation (GenMI), create opportunities to automate parts of this complex process. In this Perspective, we synthesize progress and challenges in developing AI systems for generation of medical reports from images. We focus extensively on radiology as a domain with enormous reporting needs and research efforts. In addition to analysing the strengths and applications of new models for medical report generation, we advocate for a novel paradigm to deploy GenMI in a manner that empowers clinicians and their patients. Initial research suggests that GenMI could one day match human expert performance in generating reports across disciplines, such as radiology, pathology and dermatology. However, formidable obstacles remain in validating model accuracy, ensuring transparency and eliciting nuanced impressions. If carefully implemented, GenMI could meaningfully assist clinicians in improving quality of care, enhancing medical education, reducing workloads, expanding specialty access and providing real-time expertise. Overall, we highlight opportunities alongside key challenges for developing multimodal generative AI that complements human experts for reliable medical report writing.
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Affiliation(s)
- Vishwanatha M Rao
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Hla
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Computer Science, Harvard College, Cambridge, MA, USA
| | - Michael Moor
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland
| | - Subathra Adithan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Radiodiagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India
| | - Stephen Kwak
- Department of Radiology, Johns Hopkins University, Baltimore, MD, USA
| | | | - Pranav Rajpurkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Chen J, Yan AS. Hospital Artificial Intelligence/Machine Learning Adoption by Neighborhood Deprivation. Med Care 2025; 63:227-233. [PMID: 39947693 PMCID: PMC11809723 DOI: 10.1097/mlr.0000000000002110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2025]
Abstract
OBJECTIVE To understand the variation in artificial intelligence/machine learning (AI/ML) adoption across different hospital characteristics and explore how AI/ML is utilized, particularly in relation to neighborhood deprivation. BACKGROUND AI/ML-assisted care coordination has the potential to reduce health disparities, but there is a lack of empirical evidence on AI's impact on health equity. METHODS We used linked datasets from the 2022 American Hospital Association Annual Survey and the 2023 American Hospital Association Information Technology Supplement. The data were further linked to the 2022 Area Deprivation Index (ADI) for each hospital's service area. State fixed-effect regressions were employed. A decomposition model was also used to quantify predictors of AI/ML implementation, comparing hospitals in higher versus lower ADI areas. RESULTS Hospitals serving the most vulnerable areas (ADI Q4) were significantly less likely to apply ML or other predictive models (coef = -0.10, P = 0.01) and provided fewer AI/ML-related workforce applications (coef = -0.40, P = 0.01), compared with those in the least vulnerable areas. Decomposition results showed that our model specifications explained 79% of the variation in AI/ML adoption between hospitals in ADI Q4 versus ADI Q1-Q3. In addition, Accountable Care Organization affiliation accounted for 12%-25% of differences in AI/ML utilization across various measures. CONCLUSIONS The underuse of AI/ML in economically disadvantaged and rural areas, particularly in workforce management and electronic health record implementation, suggests that these communities may not fully benefit from advancements in AI-enabled health care. Our results further indicate that value-based payment models could be strategically used to support AI integration.
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Affiliation(s)
- Jie Chen
- Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD
- The Hospital And Public health InterdisciPlinarY research (HAPPY) Lab, School of Public Health, University of Maryland, College Park, MD
- University of Maryland Center on Aging, School of Public Health, University of Maryland, College Park, MD
| | - Alice Shijia Yan
- Department of Health Policy and Management, School of Public Health, University of Maryland, College Park, MD
- The Hospital And Public health InterdisciPlinarY research (HAPPY) Lab, School of Public Health, University of Maryland, College Park, MD
- University of Maryland Center on Aging, School of Public Health, University of Maryland, College Park, MD
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Ye DH, Kim TW, Kim SM, Seo JW, Chang J, Lee JG, Ko EJ. Can AI-Based Video Analysis Help Evaluate the Performance of the Items in the Bayley Scales of Infant Development? CHILDREN (BASEL, SWITZERLAND) 2025; 12:276. [PMID: 40150561 PMCID: PMC11941028 DOI: 10.3390/children12030276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2025] [Revised: 02/18/2025] [Accepted: 02/20/2025] [Indexed: 03/29/2025]
Abstract
Aims: To develop and evaluate a novel AI-based video analysis tool for the quantitative assessment of "Places Pegs in" and "Blue Board" tasks in the Bayley Scales of Infant Development (BSID-II). Methods: A prospective cohort study was conducted from February 2022 to December 2022, including children aged 12-42 months referred for suspected developmental delay. Participants were evaluated using the BSID-II, and their performances on the two tasks were video recorded and analyzed with the novel tool. Sensitivity and specificity were determined by comparing the tool's results to standard BSID-II assessments by therapists. Data collected included total time, number of trials, successful trials, and time and spatial intervals for each trial. Children were classified into typically developing (TD) (MDI ≥ 85) and developmental delay (DD) (MDI < 85) groups based on their mental developmental index (MDI). Results: A total of 75 children participated in the study, and the mean values of MDI and PDI for the enrolled children were 88.9 ± 18.7 and 80.0 ± 16.7. The "Places Pegs in" had 86.5% sensitivity and 100% specificity; the "Blue Board" had 96.9% sensitivity and 89.5% specificity. Differences in cumulative successes over time were observed between age groups and TD and DD groups. The tool automatically calculated maximum successes at specific time points. Interpretation: The AI-based tool showed high predictive accuracy for BSID-II tasks in children aged 12-42 months, indicating potential utility for developmental assessments.
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Affiliation(s)
- Dong Hyun Ye
- Department of Rehabilitation Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea; (D.H.Y.); (J.C.)
| | - Tae Won Kim
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
| | - Su Min Kim
- Department of Rehabilitation Medicine, Asan Medical Center, Seoul 05505, Republic of Korea; (S.M.K.); (J.W.S.)
| | - Ji Won Seo
- Department of Rehabilitation Medicine, Asan Medical Center, Seoul 05505, Republic of Korea; (S.M.K.); (J.W.S.)
| | - Jongyoon Chang
- Department of Rehabilitation Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea; (D.H.Y.); (J.C.)
| | - June-Goo Lee
- Department of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea;
| | - Eun Jae Ko
- Department of Rehabilitation Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea; (D.H.Y.); (J.C.)
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Maloba M, Finocchario-Kessler S, Wexler C, Staggs V, Maosa N, Babu S, Goggin K, Hutton D, Ganda G, Mabeya H, Robertson E, Mabachi N. The Cancer Tracking System (CATSystem): Study protocol of a randomized control trial to evaluate a systems level intervention for cervical cancer screening, treatment, referral and follow up in Kenya. PLoS One 2025; 20:e0318941. [PMID: 39965035 PMCID: PMC11835318 DOI: 10.1371/journal.pone.0318941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2025] [Accepted: 01/21/2025] [Indexed: 02/20/2025] Open
Abstract
BACKGROUND Cervical cancer (CC) is preventable, yet remains a significant public health threat, particularly in Sub-Saharan Africa. Despite considerable awareness, screening rates for CC in Kenya are low and loss to follow-up following treatment for premalignant cervical lesions remains high. This study investigates the efficacy of the Cancer Tracking System (CATSystem), a web-based intervention, to improve CC screening and treatment retention. METHODS A matched, cluster randomized controlled trial will be conducted in Kenyan government hospitals (n = 10) with five intervention and five standard-of-care (SOC) sites. The primary outcome is the proportion of women with a positive screen who receive appropriate treatment (onsite or referral). Secondary outcomes include CC screening uptake among all women and timeliness of treatment initiation. We will utilize mixed methods to assess intervention feasibility, acceptability, and cost-effectiveness. DISCUSSION The CATSystem has the potential to improve CC care in Kenya by leveraging existing technology to address known barriers in the screening and treatment cascade. This study will provide valuable evidence for potential scale-up of the intervention.
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Affiliation(s)
- May Maloba
- Global Health Innovations, Nairobi, Kenya
| | - Sarah Finocchario-Kessler
- Department of Family Medicine and Community Health, The University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Catherine Wexler
- Department of Family Medicine and Community Health, The University of Kansas Medical Center, Kansas City, Kansas, United States of America
| | - Vincent Staggs
- International Drug Development Institute, Raleigh, North Carolina, United States of America
| | | | | | - Kathy Goggin
- Department of Psychology, San Diego State University, San Diego, California, United States of America
| | - David Hutton
- School of Public Health, The University of Michigan, Ann Arbor, Michigan, United States of America
| | | | - Hilary Mabeya
- Gynocare Womens and Fistula Hospital, Eldoret, Kenya
| | - Elise Robertson
- The DartNet Institute, Aurora, Colorado, United States of America
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12
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Wei Q, Pan S, Liu X, Hong M, Nong C, Zhang W. The integration of AI in nursing: addressing current applications, challenges, and future directions. Front Med (Lausanne) 2025; 12:1545420. [PMID: 40007584 PMCID: PMC11850350 DOI: 10.3389/fmed.2025.1545420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2024] [Accepted: 01/13/2025] [Indexed: 02/27/2025] Open
Abstract
Artificial intelligence is increasingly influencing healthcare, providing transformative opportunities and challenges for nursing practice. This review critically evaluates the integration of AI in nursing, focusing on its current applications, limitations, and areas that require further investigation. A comprehensive analysis of recent studies highlights the use of AI in clinical decision support systems, patient monitoring, and nursing education. However, several barriers to successful implementation are identified, including technical constraints, ethical dilemmas, and the need for workforce adaptation. Significant gaps in the literature are also evident, such as the limited development of nursing-specific AI tools, insufficient long-term impact assessments, and the absence of comprehensive ethical frameworks tailored to nursing contexts. The potential of AI to reshape personalized care, advance robotics in nursing, and address global health challenges is explored in depth. This review integrates existing knowledge and identifies critical areas for future research, emphasizing the necessity of aligning AI advancements with the specific needs of nursing. Addressing these gaps is essential to fully harness AI's potential while reducing associated risks, ultimately enhancing nursing practice and improving patient outcomes.
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Affiliation(s)
- Qiuying Wei
- Anesthesia Surgery Center, The First Affiliated Hospital of Guangxi Medical University, Naning, Guangxi, China
| | - Songcheng Pan
- Anesthesia Surgery Center, The First Affiliated Hospital of Guangxi Medical University, Naning, Guangxi, China
- Guangdong Lingnan Nightingale Nursing Academy, Guangzhou, Guangdong, China
| | - Xiaoyu Liu
- Anesthesia Surgery Center, The First Affiliated Hospital of Guangxi Medical University, Naning, Guangxi, China
| | - Mei Hong
- Anesthesia Surgery Center, The First Affiliated Hospital of Guangxi Medical University, Naning, Guangxi, China
| | - Chunying Nong
- Anesthesia Surgery Center, The First Affiliated Hospital of Guangxi Medical University, Naning, Guangxi, China
| | - Weiqi Zhang
- Anesthesia Surgery Center, The First Affiliated Hospital of Guangxi Medical University, Naning, Guangxi, China
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Ramezani M, Mobinizadeh M, Bakhtiari A, Rabiee HR, Ramezani M, Mostafavi H, Olyaeemanesh A, Fazaeli AA, Atashi A, Sazgarnejad S, Mohamadi E, Takian A. Agenda setting for health equity assessment through the lenses of social determinants of health using machine learning approach: a framework and preliminary pilot study. BioData Min 2025; 18:14. [PMID: 39930525 PMCID: PMC11808983 DOI: 10.1186/s13040-025-00428-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Accepted: 01/28/2025] [Indexed: 02/14/2025] Open
Abstract
INTRODUCTION The integration of Artificial Intelligence (AI) and Machine Learning (ML) is transforming public health by enhancing the assessment and mitigation of health inequities. As the use of AI tools, especially ML techniques, rises, they play a pivotal role in informing policies that promote a more equitable society. This study aims to develop a framework utilizing ML to analyze health system data and set agendas for health equity interventions, focusing on social determinants of health (SDH). METHOD This study utilized the CRISP-ML(Q) model to introduce a platform for health equity assessment, facilitating its design and implementation in health systems. Initially, a conceptual model was developed through a comprehensive literature review and document analysis. A pilot implementation was conducted to test the feasibility and effectiveness of using ML algorithms in assessing health equity. Life expectancy was chosen as the health outcome for this pilot; data from 2000 to 2020 with 140 features was cleaned, transformed, and prepared for modeling. Multiple ML models were developed and evaluated using SPSS Modeler software version 18.0. RESULTS ML algorithms effectively identified key SDH influencing life expectancy. Among algorithms, the Linear Discriminant algorithm as classification model was selected as the best model due to its high accuracy in both testing and training phases, its strong performance in identifying key features, and its good generalizability to new data. Additionally, CHAID in numeric models was the best for predicting the actual value of life expectancy based on various features. These models highlighted the importance of features like current health expenditure, domestic general government health expenditure, and GDP in predicting life expectancy. CONCLUSION The findings underscore the significance of employing innovative methods like CRISP-ML(Q) and ML algorithms to enhance health equity. Integrating this platform into health systems can help countries better prioritize and address health inequities. The pilot implementation demonstrated these methods' practical applicability and effectiveness, aiding policymakers in making informed decisions to improve health equity.
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Affiliation(s)
- Maryam Ramezani
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | | | - Ahad Bakhtiari
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Hamid R Rabiee
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Maryam Ramezani
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
| | - Hakimeh Mostafavi
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Olyaeemanesh
- National Institute for Health Research, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Akbar Fazaeli
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Atashi
- E‑Health Department, Virtual School, Tehran University of Medical Science, Tehran, Iran
| | - Saharnaz Sazgarnejad
- School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Efat Mohamadi
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Amirhossein Takian
- Department of Health Management, Policy and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
- Health Equity Research Centre (HERC), Tehran University of Medical Sciences, Tehran, Iran.
- Centre of Excellence for Global Health (CEGH), Department of Global Health and Public Policy, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
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Aldhahi MI, Alorainy AI, Abuzaid MM, Gareeballah A, Alsubaie NF, Alshamary AS, Hamd ZY. Adoption of Artificial Intelligence in Rehabilitation: Perceptions, Knowledge, and Challenges Among Healthcare Providers. Healthcare (Basel) 2025; 13:350. [PMID: 39997225 PMCID: PMC11855079 DOI: 10.3390/healthcare13040350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2024] [Revised: 01/27/2025] [Accepted: 02/04/2025] [Indexed: 02/26/2025] Open
Abstract
BACKGROUND/OBJECTIVES The current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive and prepare to implement artificial intelligence (AI) in their practices. Therefore, we conducted a cross-sectional observational study to assess the perceptions, knowledge, and willingness of rehabilitation healthcare providers to implement AI in practice. METHODS This study was conducted in Saudi Arabia, with data collected from 430 physical therapy professionals via an online SurveyMonkey questionnaire between January and March 2024. The survey assessed demographics, AI knowledge and skills, and perceived challenges. Data were analyzed using Statistical Package for the Social Science (SPSS 27) and DATAtab (version 2025), with frequencies, percentages, and nonparametric tests used to examine the relationships between the variables. RESULTS The majority of respondents (80.9%) believed that AI would be integrated into physical therapy in future, with 78.6% seeing AI as significantly impacting their work. While 61.4% thought that AI would reduce workload and enhance productivity, only 30% expressed concerns about AI endangering their profession. A lack of formal AI training has commonly been reported, with social media platforms being respondents' primary source of AI knowledge. Despite these challenges, 85.1% expressed an eagerness to learn and use AI. Organizational preparedness was a significant barrier, with 45.6% of respondents reporting that their organizations lacked AI strategies. There were insignificant differences in the mean rank of AI perceptions or knowledge based on the gender, years of experience, and qualification degree of the respondents. CONCLUSIONS The results demonstrated a strong interest in AI implementation in physical therapy. The majority of respondents expressed confidence in AI's future utility and readiness to incorporate it into their practice. However, challenges, such as a lack of formal training and organizational preparedness, were identified. Overall, the findings highlight AI's potential to revolutionize physical therapy while underscoring the necessity to address training and readiness to fully realize this potential.
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Affiliation(s)
- Monira I. Aldhahi
- Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Amal I. Alorainy
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (A.I.A.); (N.F.A.); (Z.Y.H.)
| | - Mohamed M. Abuzaid
- Medical Diagnostic Imaging Department, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates;
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates
| | - Awadia Gareeballah
- Department of Diagnostic Radiology, College of Applied Medical Science, Taibah University, P.O. Box 344, Al-Madinah Al-Munawwarah 41477, Saudi Arabia;
| | - Naifah F. Alsubaie
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (A.I.A.); (N.F.A.); (Z.Y.H.)
| | | | - Zuhal Y. Hamd
- Department of Radiological Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia; (A.I.A.); (N.F.A.); (Z.Y.H.)
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15
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Skok K, Bräutigam K. Tumor infiltrating lymphocytes (TILs) - Pathologia, quo vadis? - A global survey. Pathol Res Pract 2025; 266:155775. [PMID: 39700663 DOI: 10.1016/j.prp.2024.155775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2024] [Revised: 11/27/2024] [Accepted: 12/12/2024] [Indexed: 12/21/2024]
Abstract
Tumor-infiltrating lymphocytes (TILs) and the tumor microenvironment have become increasingly important in cancer research, and immunotherapy has achieved major breakthroughs in improving patient outcomes. Despite the significant role of the pathologist in identifying, subtyping and reporting TILs, the implementation and assessment of TILs in pathology routine remains vague. To assess the actual use of TILs in routine clinical practice, a formal standardized questionnaire was disseminated on two social media platforms ("X" and LinkedIn) and by email in June 2024. Based on the results, we conducted a literature review on TILs via Medline/Pubmed in the two most scored and reported entities, namely malignant melanoma and colorectal cancer (CRC). 77 participants from 24 different countries around the world, mostly pathologists (n = 63, 82.0 %), completed the survey. More than half of the participants do not assess or report TILs in their daily (clinical) practice, a trend consistent across the countries included in the study. A variety of methods are used to report TILs, ranging from Artificial Intelligence (AI)-based scoring algorithms to quantification by eyeballing. Despite recognizing the importance of TIL assessment in clinical routine, many participants find it time-consuming and express a strong preference for AI-based quantification. Our survey reflects the perspective of mostly early career pathologists who recognize the importance of TILs in cancer but face challenges in implementation. The development of AI tools and consensus guidelines could alleviate these barriers. In addition, increasing the visibility and understanding of the role of pathologists within the medical community remains critical.
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Affiliation(s)
- Kristijan Skok
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Stiftingtalstraße 6, Graz 8010, Austria; Institute of Biomedical Sciences, Faculty of Medicine, University of Maribor, Taborska Ulica 8, Maribor 2000, Slovenia
| | - Konstantin Bräutigam
- Centre for Evolution and Cancer, Institute of Cancer Research, London, SM2 5NG, United Kingdom.
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16
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Dankwa-Mullan I, Ndoh K, Akogo D, Rocha HAL, Juaçaba SF. Artificial Intelligence and Cancer Health Equity: Bridging the Divide or Widening the Gap. Curr Oncol Rep 2025; 27:95-111. [PMID: 39753817 DOI: 10.1007/s11912-024-01627-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2024] [Indexed: 02/26/2025]
Abstract
PURPOSE OF REVIEW This review aims to evaluate the impact of artificial intelligence (AI) on cancer health equity, specifically investigating whether AI is addressing or widening disparities in cancer outcomes. RECENT FINDINGS Recent studies demonstrate significant advancements in AI, such as deep learning for cancer diagnosis and predictive analytics for personalized treatment, showing potential for improved precision in care. However, concerns persist about the performance of AI tools across diverse populations due to biased training data. Access to AI technologies also remains limited, particularly in low-income and rural settings. AI holds promise for advancing cancer care, but its current application risks exacerbating existing health disparities. To ensure AI benefits all populations, future research must prioritize inclusive datasets, integrate social determinants of health, and develop ethical frameworks. Addressing these challenges is crucial for AI to contribute positively to cancer health equity and guide future research and policy development.
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Affiliation(s)
- Irene Dankwa-Mullan
- Milken Institute School of Public Health, Department of Health Policy and Management, George Washington University, Washington D.C., USA.
| | - Kingsley Ndoh
- Hurone AI, Seattle, Washington, USA
- Department of Global Health, University of Washington, Seattle, USA
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Campbell EA, Holl F, Marwah HK, Fraser HS, Craig SS. The impact of climate change on vulnerable populations in pediatrics: opportunities for AI, digital health, and beyond-a scoping review and selected case studies. Pediatr Res 2025:10.1038/s41390-024-03719-x. [PMID: 39881182 DOI: 10.1038/s41390-024-03719-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 05/30/2024] [Accepted: 08/07/2024] [Indexed: 01/31/2025]
Abstract
Climate change critically impacts global pediatric health, presenting unique and escalating challenges due to children's inherent vulnerabilities and ongoing physiological development. This scoping review intricately intertwines the spheres of climate change, pediatric health, and Artificial Intelligence (AI), with a goal to elucidate the potential of AI and digital health in mitigating the adverse child health outcomes induced by environmental alterations, especially in Low- and Middle-Income Countries (LMICs). A notable gap is uncovered: literature directly correlating AI interventions with climate change-impacted pediatric health is scant, even though substantial research exists at the confluence of AI and health, and health and climate change respectively. We present three case studies about AI's promise in addressing pediatric health issues exacerbated by climate change. The review spotlights substantial obstacles, including technical, ethical, equitable, privacy, and data security challenges in AI applications for pediatric health, necessitating in-depth, future-focused research. Engaging with the intricate nexus of climate change, pediatric health, and AI, this work underpins future explorations into leveraging AI to navigate and neutralize the burgeoning impact of climate change on pediatric health outcomes. IMPACT: Our scoping review highlights the scarcity of literature directly correlating AI interventions with climate change-impacted pediatric health that disproportionately affects vulnerable populations, even though substantial research exists at the confluence of AI and health, and health and climate change respectively. We present three case studies about AI's promise in addressing pediatric health issues exacerbated by climate change. The review spotlights substantial obstacles, including technical, ethical, equitable, privacy, and data security challenges in AI applications for pediatric health, necessitating in-depth, future-focused research.
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Affiliation(s)
- Elizabeth A Campbell
- Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD, 21205, USA.
- Center for Outbreak Response Innovation, Johns Hopkins Bloomberg School of Public Health, 700 E. Pratt Street, Suite 900, Baltimore, MD, 21202, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, 622 W 168th St PH20 3720, New York, NY, 10032, USA.
| | - Felix Holl
- DigiHealth Institute, Neu-Ulm University of Applied Sciences, Neu-Ulm, Germany
| | - Harleen K Marwah
- Division of General Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Hamish S Fraser
- Brown Center for Biomedical Informatics, The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Sansanee S Craig
- Division of General Pediatrics, Department of Pediatrics, The Children's Hospital of Philadelphia, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.
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Kassem H, Beevi AA, Basheer S, Lutfi G, Cheikh Ismail L, Papandreou D. Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence. Nutrients 2025; 17:190. [PMID: 39796624 PMCID: PMC11723148 DOI: 10.3390/nu17010190] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2024] [Revised: 12/27/2024] [Accepted: 01/04/2025] [Indexed: 01/13/2025] Open
Abstract
BACKGROUND Artificial Intelligence (AI) technologies are now essential as the agenda of nutrition research expands its scope to look at the intricate connection between food and health in both an individual and a community context. AI also helps in tracing and offering solutions in dietary assessment, personalized and clinical nutrition, as well as disease prediction and management, such as cardiovascular diseases, diabetes, cancer, and obesity. This review aims to investigate and assess the different applications and roles of AI in nutrition and research and understand its potential future impact. METHODS We used PubMed, Scopus, Web of Science, Google Scholar, and EBSCO databases for our search. RESULTS Our findings indicate that AI is reshaping the field of nutrition in ways that were previously unimaginable. By enhancing how we assess diets, customize nutrition plans, and manage complex health conditions, AI has become an essential tool. Technologies like machine learning models, wearable devices, and chatbot applications are revolutionizing the accuracy of dietary tracking, making it easier than ever to provide tailored solutions for individuals and communities. These innovations are proving invaluable in combating diet-related illnesses and encouraging healthier eating habits. One breakthrough has been in dietary assessment, where AI has significantly reduced errors that are common in traditional methods. Tools that use visual recognition, deep learning, and mobile applications have made it possible to analyze the nutrient content of meals with incredible precision. CONCLUSIONS Moving forward, collaboration between tech developers, healthcare professionals, policymakers, and researchers will be essential. By focusing on high-quality data, addressing ethical challenges, and keeping user needs at the forefront, AI can truly revolutionize nutrition science. The potential is enormous. AI is set to make healthcare not only more effective and personalized but also more equitable and accessible for everyone.
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Affiliation(s)
- Hanin Kassem
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates; (H.K.); (A.A.B.); (S.B.); (G.L.); (L.C.I.)
| | - Aneesha Abida Beevi
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates; (H.K.); (A.A.B.); (S.B.); (G.L.); (L.C.I.)
| | - Sondos Basheer
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates; (H.K.); (A.A.B.); (S.B.); (G.L.); (L.C.I.)
| | - Gadeer Lutfi
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates; (H.K.); (A.A.B.); (S.B.); (G.L.); (L.C.I.)
| | - Leila Cheikh Ismail
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates; (H.K.); (A.A.B.); (S.B.); (G.L.); (L.C.I.)
- Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford OX1 2JD, UK
| | - Dimitrios Papandreou
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates; (H.K.); (A.A.B.); (S.B.); (G.L.); (L.C.I.)
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Fathi M, Vakili K, Hajibeygi R, Bahrami A, Behzad S, Tafazolimoghadam A, Aghabozorgi H, Eshraghi R, Bhatt V, Gholamrezanezhad A. Cultivating diagnostic clarity: The importance of reporting artificial intelligence confidence levels in radiologic diagnoses. Clin Imaging 2025; 117:110356. [PMID: 39566394 DOI: 10.1016/j.clinimag.2024.110356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2024] [Revised: 11/01/2024] [Accepted: 11/09/2024] [Indexed: 11/22/2024]
Abstract
Accurate image interpretation is essential in the field of radiology to the healthcare team in order to provide optimal patient care. This article discusses the use of artificial intelligence (AI) confidence levels to enhance the accuracy and dependability of its radiological diagnoses. The current advances in AI technologies have changed how radiologists and clinicians make the diagnoses of pathological conditions such as aneurysms, hemorrhages, pneumothorax, pneumoperitoneum, and particularly fractures. To enhance the utility of these AI models, radiologists need a more comprehensive understanding of the model's levels of confidence and certainty behind the results they produce. This allows radiologists to make more informed decisions that have the potential to drastically change a patient's clinical management. Several AI models, especially those utilizing deep learning models (DL) with convolutional neural networks (CNNs), have demonstrated significant potential in identifying subtle findings in medical imaging that are often missed by radiologists. It is necessary to create standardized levels of confidence metrics in order for AI systems to be relevant and reliable in the clinical setting. Incorporating AI into clinical practice does have certain obstacles like the need for clinical validation, concerns regarding the interpretability of AI system results, and addressing confusion and misunderstandings within the medical community. This study emphasizes the importance of AI systems to clearly convey their level of confidence in radiological diagnosis. This paper highlights the importance of conducting research to establish AI confidence level metrics that are limited to a specific anatomical region or lesion type. KEY POINT OF THE VIEW: Accurate fracture diagnosis relies on radiologic certainty, where Artificial intelligence (AI), especially convolutional neural networks (CNNs) and deep learning (DL), shows promise in enhancing X-ray interpretation amidst a shortage of radiologists. Overcoming integration challenges through improved AI interpretability and education is crucial for widespread acceptance and better patient outcomes.
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Affiliation(s)
- Mobina Fathi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Science, Tehran, Iran; School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kimia Vakili
- School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ramtin Hajibeygi
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Science, Tehran, Iran; Tehran University of Medical Science (TUMS), School of Medicine, Tehran, Iran
| | - Ashkan Bahrami
- Faculty of Medicine, Kashan University of Medical Science, Kashan, Iran
| | - Shima Behzad
- Advanced Diagnostic and Interventional Radiology Research Center (ADIR), Tehran University of Medical Science, Tehran, Iran
| | | | - Hadiseh Aghabozorgi
- Student Research Committee, Shahrekord University of Medical Sciences, Shahrekord, Iran
| | - Reza Eshraghi
- Faculty of Medicine, Kashan University of Medical Science, Kashan, Iran
| | - Vivek Bhatt
- University of California, Riverside, School of Medicine, Riverside, CA, United States of America
| | - Ali Gholamrezanezhad
- Keck School of Medicine of University of Southern California, Los Angeles, CA, United States of America; Department of Radiology, Cedars Sinai Hospital, Los Angeles, CA, United States of America.
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Sperling J, Welsh W, Haseley E, Quenstedt S, Muhigaba PB, Brown A, Ephraim P, Shafi T, Waitzkin M, Casarett D, Goldstein BA. Machine learning-based prediction models in medical decision-making in kidney disease: patient, caregiver, and clinician perspectives on trust and appropriate use. J Am Med Inform Assoc 2025; 32:51-62. [PMID: 39545362 DOI: 10.1093/jamia/ocae255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/22/2024] [Accepted: 09/25/2024] [Indexed: 11/17/2024] Open
Abstract
OBJECTIVES This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups. MATERIALS AND METHODS We collected and analyzed qualitative data from focus groups with varied end users, including: dialysis support providers (clinical providers and additional dialysis support providers such as dialysis clinic staff and social workers); patients; patients' caregivers (n = 52). RESULTS Participants were broadly accepting of ML-based CPMs, but with concerns on data sources, factors included in the model, and accuracy. Use was desired in conjunction with providers' views and explanations. Differences among respondent types were minimal overall but most prevalent in discussions of CPM presentation and model use. DISCUSSION AND CONCLUSION Evidence of acceptability of ML-based CPM usage provides support for ethical use, but numerous specific considerations in acceptability, model construction, and model use for shared clinical decision-making must be considered. There are specific steps that could be taken by data scientists and health systems to engender use that is accepted by end users and facilitates trust, but there are also ongoing barriers or challenges in addressing desires for use. This study contributes to emerging literature on interpretability, mechanisms for sharing complexities, including uncertainty regarding the model results, and implications for decision-making. It examines numerous stakeholder groups including providers, patients, and caregivers to provide specific considerations that can influence health system use and provide a basis for future research.
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Affiliation(s)
- Jessica Sperling
- Social Science Research Institute, Duke University, Durham, NC 27708, United States
- Clinical and Translational Science Institute, Duke University School of Medicine, Durham, NC 27701, United States
- Department of Medicine, Duke University School of Medicine, Durham, NC 27708, United States
| | - Whitney Welsh
- Social Science Research Institute, Duke University, Durham, NC 27708, United States
| | - Erin Haseley
- Social Science Research Institute, Duke University, Durham, NC 27708, United States
| | - Stella Quenstedt
- Clinical and Translational Science Institute, Duke University School of Medicine, Durham, NC 27701, United States
| | - Perusi B Muhigaba
- Clinical and Translational Science Institute, Duke University School of Medicine, Durham, NC 27701, United States
| | - Adrian Brown
- Social Science Research Institute, Duke University, Durham, NC 27708, United States
| | - Patti Ephraim
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY 11030, United States
| | - Tariq Shafi
- Department of Medicine, Houston Methodist, Houston, TX 77030, United States
| | - Michael Waitzkin
- Science & Society, Duke University, Durham, NC 27708, United States
| | - David Casarett
- Department of Medicine, Duke University School of Medicine, Durham, NC 27708, United States
| | - Benjamin A Goldstein
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27708, United States
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Chaparala SP, Pathak KD, Dugyala RR, Thomas J, Varakala SP. Leveraging Artificial Intelligence to Predict and Manage Complications in Patients With Multimorbidity: A Literature Review. Cureus 2025; 17:e77758. [PMID: 39981468 PMCID: PMC11840652 DOI: 10.7759/cureus.77758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/21/2025] [Indexed: 02/22/2025] Open
Abstract
Artificial intelligence (AI) is revolutionizing healthcare by improving diagnostic accuracy, streamlining treatment protocols, and augmenting patient care, especially in the management of multimorbidity. This review assesses the applications of AI in forecasting and controlling problems in multimorbid patients, emphasizing predictive analytics, real-time data integration, and enhancements in diagnostics. Utilizing extensive datasets from electronic health records and medical imaging, AI models facilitate early complication prediction and prompt therapies in diseases such as cancer, cardiovascular disorders, and diabetes. Notable developments encompass AI systems for the diagnosis of lung and breast cancer, markedly decreasing false positives and minimizing superfluous follow-ups. A comprehensive literature search was performed via PubMed and Google Scholar, applying Boolean logic with keywords such as "artificial intelligence", "multimorbidity", "predictive analytics", "machine learning", and "diagnosis". Articles published in English from January 2010 to December 2024, encompassing original research, systematic reviews, and meta-analyses regarding the use of AI in managing multimorbidity and healthcare decision-making, were included. Studies not pertinent to therapeutic applications, devoid of outcome measurements, or restricted to editorials were discarded. This review emphasizes AI's capacity to augment diagnostic precision and boost clinical results while also identifying substantial hurdles, including data bias, ethical issues, and the necessity for rigorous validation and longitudinal research to guarantee sustainable integration in clinical environments. This review's limitations encompass the possible exclusion of pertinent studies due to language and publication year constraints, as well as the disregard for grey literature, potentially constraining the comprehensiveness of the findings.
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Affiliation(s)
- Sai Praneeth Chaparala
- Internal Medicine, Gayatri Vidya Parishad Institute of Health Care and Medical Technology, Visakhapatnam, IND
| | - Kesha D Pathak
- Medicine, Gujarat Adani Institute of Medical Sciences, Bhuj, IND
| | | | - Joel Thomas
- Internal Medicine, RAK Medical and Health Sciences University, Ras Al Khaimah, ARE
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22
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Oikonomou EK, Khera R. Designing medical artificial intelligence systems for global use: focus on interoperability, scalability, and accessibility. Hellenic J Cardiol 2025; 81:9-17. [PMID: 39025234 DOI: 10.1016/j.hjc.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/21/2024] [Accepted: 07/02/2024] [Indexed: 07/20/2024] Open
Abstract
Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA; Center for Outcomes Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
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23
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Ajluni V. Artificial intelligence in psychiatric education: Enhancing clinical competence through simulation. Ind Psychiatry J 2025; 34:11-15. [PMID: 40376628 PMCID: PMC12077637 DOI: 10.4103/ipj.ipj_377_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 10/02/2024] [Accepted: 10/07/2024] [Indexed: 05/18/2025] Open
Abstract
The integration of artificial intelligence (AI) in psychiatric education offers transformative potential to enhance clinical competence through realistic simulations. Traditional educational methods face limitations in replicating complex psychiatric cases, and AI-based tools provide a scalable solution. This narrative review examines current evidence on the efficacy of AI-powered simulations, focusing on their role in skill development, diagnostic accuracy, and safe clinical training. Through a comprehensive literature review of studies from 2010 to 2024, key themes such as AI's ability to standardize patient encounters, provide instant feedback, and improve student confidence are explored. Findings suggest that AI can enhance psychiatric education by offering consistent, adaptable learning experiences that prepare trainees for real-world complexities. However, challenges such as ethical considerations and accessibility disparities must be addressed for AI to be effectively integrated into psychiatric training. This review provides insights into the future of AI in medical education and its potential impact on training the next generation of psychiatrists.
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Affiliation(s)
- Victor Ajluni
- Department of Psychiatry, Wayne State University, Detroit, Michigan, Livonia, MI, USA
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24
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Arnout BA, Alshehri SM. Causal Relationships Between the Use of AI, Therapeutic Alliance, and Job Engagement Among Psychological Service Practitioners. Behav Sci (Basel) 2024; 15:21. [PMID: 39851825 PMCID: PMC11762742 DOI: 10.3390/bs15010021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2024] [Revised: 12/21/2024] [Accepted: 12/23/2024] [Indexed: 01/26/2025] Open
Abstract
Despite the significant increase in studies on AI applications in many aspects of life, its applications in mental health services still require further studies. This study aimed to test a proposed structural model of the relationships between AI use, therapeutic alliance, and job engagement by PLS-SEM. The descriptive method was applied. The sample consisted of (382) mental health service providers in Saudi Arabia, including 178 men and 204 women between 25 and 50 (36.32 ± 6.43) years old. The Artificial Intelligence Questionnaire, the Therapeutic Alliance Scale, and the Job Engagement Scale were applied in this study. The results showed the structural model's predictability for using AI and the therapeutic alliance in predicting job engagement and explaining the causal relationships between them compared to the indicator average and linear models. The study also found a strong positive overall statistically significant effect (p < 0.05) of the use of AI on therapeutic alliance (0.941) and job engagement (0.930) and a positive overall average statistically significant effect (p < 0.05) of the therapeutic alliance on job engagement (0.694). These findings indicated the importance of integrating AI applications and therapeutic alliance skills into training and professional development plans.
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Affiliation(s)
- Boshra A. Arnout
- Department of Psychology, College of Education, King Khalid University, P.O. Box 2380, Abha 62521, Saudi Arabia
- Department of Psychology, College of Arts, Zagazig University, Zagazig 44511, Egypt
| | - Sami M. Alshehri
- Department of Learning and Instructor, College of Education, King Khalid University, P.O. Box 8685, Abha 61492, Saudi Arabia;
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25
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Strika Z, Petkovic K, Likic R, Batenburg R. Bridging healthcare gaps: a scoping review on the role of artificial intelligence, deep learning, and large language models in alleviating problems in medical deserts. Postgrad Med J 2024; 101:4-16. [PMID: 39323384 DOI: 10.1093/postmj/qgae122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 08/08/2024] [Accepted: 09/04/2024] [Indexed: 09/27/2024]
Abstract
"Medical deserts" are areas with low healthcare service levels, challenging the access, quality, and sustainability of care. This qualitative narrative review examines how artificial intelligence (AI), particularly large language models (LLMs), can address these challenges by integrating with e-Health and the Internet of Medical Things to enhance services in under-resourced areas. It explores AI-driven telehealth platforms that overcome language and cultural barriers, increasing accessibility. The utility of LLMs in providing diagnostic assistance where specialist deficits exist is highlighted, demonstrating AI's role in supplementing medical expertise and improving outcomes. Additionally, the development of AI chatbots offers preliminary medical advice, serving as initial contact points in remote areas. The review also discusses AI's role in enhancing medical education and training, supporting the professional development of healthcare workers in these regions. It assesses AI's strategic use in data analysis for effective resource allocation, identifying healthcare provision gaps. AI, especially LLMs, is seen as a promising solution for bridging healthcare gaps in "medical deserts," improving service accessibility, quality, and distribution. However, continued research and development are essential to fully realize AI's potential in addressing the challenges of medical deserts.
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Affiliation(s)
- Zdeslav Strika
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
| | - Karlo Petkovic
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
| | - Robert Likic
- University of Zagreb School of Medicine, Salata 3, Zagreb 10000, Croatia
- Department of Internal Medicine, Division of Clinical Pharmacology, Clinical Hospital Centre Zagreb, Kispaticeva 12, Zagreb 10000, Croatia
| | - Ronald Batenburg
- Netherlands Institute for Health Services Research (NIVEL), Otterstraat 118, Utrecht 3553, The Netherlands
- Department of Sociology, Radboud University, Thomas Van Aquinostraat 4, Nijmegen 6524, The Netherlands
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26
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Gbagbo FY, Ameyaw EK, Yaya S. Artificial intelligence and sexual reproductive health and rights: a technological leap towards achieving sustainable development goal target 3.7. Reprod Health 2024; 21:196. [PMID: 39716281 DOI: 10.1186/s12978-024-01924-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2024] [Accepted: 11/30/2024] [Indexed: 12/25/2024] Open
Abstract
Target 3.7 of the Sustainable Development Goals (SDGs) aims for universal access to sexual and reproductive health (SRH) services by 2030, including family planning services, information, education, and integration into national strategies. In contemporary times, reproductive medicine is progressively incorporating artificial intelligence (AI) to enhance sperm cell prediction and selection, in vitro fertilisation models, infertility and pregnancy screening. AI is being integrated into five core components of Sexual Reproductive Health, including improving care, providing high-quality contraception and infertility services, eliminating unsafe abortions, as well as facilitating the prevention and treatment of sexually transmitted infections. Though AI can improve sexual reproductive health and rights by addressing disparities and enhancing service delivery, AI-facilitated components have ethical implications, based on existing human rights and international conventions. Heated debates persist in implementing AI, particularly in maternal health, as well as sexual, reproductive health as the discussion centers on a torn between human touch and machine-driven care. In spite of this and other challenges, AI's application in sexual, and reproductive health and rights is crucial, particularly for developing countries, especially those that are yet to explore the application of AI in healthcare. Action plans are needed to roll out AI use in these areas effectively, and capacity building for health workers is essential to achieve the Sustainable Development Goals' Target 3.7. This commentary discusses innovations in sexual, and reproductive health and rights in meeting target 3.7 of the SDGs with a focus on artificial intelligence and highlights the need for a more circumspective approach in response to the ethical and human rights implications of using AI in providing sexual and reproductive health services.
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Affiliation(s)
| | - Edward Kwabena Ameyaw
- Institute of Policy Studies and School of Graduate Studies, Lingnan University, Tuen Mun, Hong Kong
| | - Sanni Yaya
- The George Institute for Global Health, Imperial College London, London, UK.
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27
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Alqahtani MM, Alanazi AMM, Algarni SS, Aljohani H, Alenezi FK, F Alotaibi T, Alotaibi M, K Alqahtani M, Alahmari M, S Alwadeai K, M Alghamdi S, Almeshari MA, Alshammari TF, Mumenah N, Al Harbi E, Al Nufaiei ZF, Alhuthail E, Alzahrani E, Alahmadi H, Alarifi A, Zaidan A, T Ismaeil T. Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review. Interact J Med Res 2024; 13:e57271. [PMID: 39705080 PMCID: PMC11699506 DOI: 10.2196/57271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Revised: 09/22/2024] [Accepted: 10/28/2024] [Indexed: 12/21/2024] Open
Abstract
BACKGROUND Artificial intelligence is experiencing rapid growth, with continual innovation and advancements in the health care field. OBJECTIVE This study aims to evaluate the application of artificial intelligence technologies across various domains of respiratory care. METHODS We conducted a narrative review to examine the latest advancements in the use of artificial intelligence in the field of respiratory care. The search was independently conducted by respiratory care experts, each focusing on their respective scope of practice and area of interest. RESULTS This review illuminates the diverse applications of artificial intelligence, highlighting its use in areas associated with respiratory care. Artificial intelligence is harnessed across various areas in this field, including pulmonary diagnostics, respiratory care research, critical care or mechanical ventilation, pulmonary rehabilitation, telehealth, public health or health promotion, sleep clinics, home care, smoking or vaping behavior, and neonates and pediatrics. With its multifaceted utility, artificial intelligence can enhance the field of respiratory care, potentially leading to superior health outcomes for individuals under this extensive umbrella. CONCLUSIONS As artificial intelligence advances, elevating academic standards in the respiratory care profession becomes imperative, allowing practitioners to contribute to research and understand artificial intelligence's impact on respiratory care. The permanent integration of artificial intelligence into respiratory care creates the need for respiratory therapists to positively influence its progression. By participating in artificial intelligence development, respiratory therapists can augment their clinical capabilities, knowledge, and patient outcomes.
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Affiliation(s)
- Mohammed M Alqahtani
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Abdullah M M Alanazi
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Saleh S Algarni
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Hassan Aljohani
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Faraj K Alenezi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Anesthesia Technology Department, College of Applied Medical Sciences, King Saud Bin Abdul-Aziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Tareq F Alotaibi
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mansour Alotaibi
- Department of Physical Therapy, Northern Border University, Arar, Saudi Arabia
| | - Mobarak K Alqahtani
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Mushabbab Alahmari
- Department of Respiratory Therapy, College of Applied Medical Sciences, University of Bisha, Bisha, Saudi Arabia
- Health and Humanities Research Center, University of Bisha, Bisha, Saudi Arabia
| | - Khalid S Alwadeai
- Department of Rehabilitation Science, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Saeed M Alghamdi
- Clinical Technology Department, Respiratory Care Program, Faculty of Applied Medical Sciences, Umm Al-Qura University, Mekkah, Saudi Arabia
| | - Mohammed A Almeshari
- Department of Rehabilitation Science, College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | | | - Noora Mumenah
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Ebtihal Al Harbi
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Ziyad F Al Nufaiei
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Jeddah, Saudi Arabia
- King Abdullah International Medical Research Center, Jeddah, Saudi Arabia
| | - Eyas Alhuthail
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Basic Sciences Department, College of Sciences and Health Professions, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Esam Alzahrani
- Department of Computer Engineering, Al-Baha University, Alaqiq, Saudi Arabia
| | - Husam Alahmadi
- Department of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Abdulaziz Alarifi
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Basic Sciences Department, College of Sciences and Health Professions, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Amal Zaidan
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Public Health, College of Public Health and Health Informatics, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Taha T Ismaeil
- Department of Respiratory Therapy, College of Applied Medical Sciences, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Department of Respiratory Services, King Abdulaziz Medical City, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
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28
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Ngaruiya C, Samad Z, Tajuddin S, Nasim Z, Leff R, Farhad A, Pires K, Khan MA, Hartz L, Safdar B. Identification of Gender Differences in Acute Myocardial Infarction Presentation and Management at Aga Khan University Hospital-Pakistan: Natural Language Processing Application in a Dataset of Patients With Cardiovascular Disease. JMIR Form Res 2024; 8:e42774. [PMID: 39705071 PMCID: PMC11699486 DOI: 10.2196/42774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/23/2023] [Accepted: 09/24/2024] [Indexed: 12/21/2024] Open
Abstract
BACKGROUND Ischemic heart disease is a leading cause of death globally with a disproportionate burden in low- and middle-income countries (LMICs). Natural language processing (NLP) allows for data enrichment in large datasets to facilitate key clinical research. We used NLP to assess gender differences in symptoms and management of patients hospitalized with acute myocardial infarction (AMI) at Aga Khan University Hospital-Pakistan. OBJECTIVE The primary objective of this study was to use NLP to assess gender differences in the symptoms and management of patients hospitalized with AMI at a tertiary care hospital in Pakistan. METHODS We developed an NLP-based methodology to extract AMI symptoms and medications from 5358 discharge summaries spanning the years 1988 to 2018. This dataset included patients admitted and discharged between January 1, 1988, and December 31, 2018, who were older than 18 years with a primary discharge diagnosis of AMI (using ICD-9 [International Classification of Diseases, Ninth Revision], diagnostic codes). The methodology used a fuzzy keyword-matching algorithm to extract AMI symptoms from the discharge summaries automatically. It first preprocesses the free text within the discharge summaries to extract passages indicating the presenting symptoms. Then, it applies fuzzy matching techniques to identify relevant keywords or phrases indicative of AMI symptoms, incorporating negation handling to minimize false positives. After manually reviewing the quality of extracted symptoms in a subset of discharge summaries through preliminary experiments, a similarity threshold of 80% was determined. RESULTS Among 1769 women and 3589 men with AMI, women had higher odds of presenting with shortness of breath (odds ratio [OR] 1.46, 95% CI 1.26-1.70) and lower odds of presenting with chest pain (OR 0.65, 95% CI 0.55-0.75), even after adjustment for diabetes and age. Presentation with abdominal pain, nausea, or vomiting was much less frequent but consistently more common in women (P<.001). "Ghabrahat," a culturally distinct term for a feeling of impending doom was used by 5.09% of women and 3.69% of men as presenting symptom for AMI (P=.06). First-line medication prescription (statin and β-blockers) was lower in women: women had nearly 30% lower odds (OR 0.71, 95% CI 0.57-0.90) of being prescribed statins, and they had 40% lower odds (OR 0.67, 95% CI 0.57-0.78) of being prescribed β-blockers. CONCLUSIONS Gender-based differences in clinical presentation and medication management were demonstrated in patients with AMI at a tertiary care hospital in Pakistan. The use of NLP for the identification of culturally nuanced clinical characteristics and management is feasible in LMICs and could be used as a tool to understand gender disparities and address key clinical priorities in LMICs.
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Affiliation(s)
- Christine Ngaruiya
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States
- Department of Emergency Medicine, Stanford School of Medicine, Palo Alto, CA, United States
| | - Zainab Samad
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Salma Tajuddin
- Department of Medicine, Aga Khan University, Karachi, Pakistan
- CITRIC Health Data Science Center, Aga Khan University, Karachi, Pakistan
| | - Zarmeen Nasim
- CITRIC Health Data Science Center, Aga Khan University, Karachi, Pakistan
| | - Rebecca Leff
- Department of Emergency Medicine, Mayo Clinic School of Graduate Medical Education, Rochester, MN, United States
| | - Awais Farhad
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Kyle Pires
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States
| | | | - Lauren Hartz
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States
| | - Basmah Safdar
- Department of Emergency Medicine, Yale School of Medicine, New Haven, CT, United States
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Periáñez Á, Fernández Del Río A, Nazarov I, Jané E, Hassan M, Rastogi A, Tang D. The Digital Transformation in Health: How AI Can Improve the Performance of Health Systems. Health Syst Reform 2024; 10:2387138. [PMID: 39437247 DOI: 10.1080/23288604.2024.2387138] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 06/27/2024] [Accepted: 07/29/2024] [Indexed: 10/25/2024] Open
Abstract
Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intelligence and Reinforcement Learning platform, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices, and to send personalized recommendations based on past data and predictions, can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be decisive, is discussed. This framework is similarly applicable to improving efficiency in health systems where scarcity is not an issue.
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30
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Charles C, Tulloch C, McNaughton M, Hosein P, Hambleton IR. Data journey map: a process for co-creating data requirements for health care artificial intelligence. Rev Panam Salud Publica 2024; 48:e107. [PMID: 39687242 PMCID: PMC11648063 DOI: 10.26633/rpsp.2024.107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2024] [Accepted: 08/12/2024] [Indexed: 12/18/2024] Open
Abstract
The Caribbean small island developing states have limited resources for comprehensive health care provision and are facing an increasing burden of noncommunicable diseases which is driven by an aging regional population. Artificial intelligence (AI) and other digital technologies offer promise for contributing to health care efficiencies, but themselves are dependent on the availability and accessibility of accurate health care data. A regional shortfall in data professionals continues to hamper legislative recognition and promotion of increased data production in Caribbean countries. Tackling the data shortfall will take time and will require a sustainably wider pool of data producers. The data journey map is one approach that can contribute to overcoming such challenges. A data journey map is a process for organizing the collection of health data that focuses on interactions between patient and health care provider. It introduces the idea that data collection is an integral part of the patient journey and that interactions between patient and provider can be enhanced by building data collection into daily health care. A carefully developed and enacted data journey map highlights key points in the care pathway for data collection. These so-called data hotspots can be used to plan - then eventually implement - appropriate AI health care solutions. In this article we introduce the idea of journey mapping, offer an example using cervical cancer prevention and treatment, and discuss the benefits and challenges to implementing such an approach.
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Affiliation(s)
- Curtis Charles
- Five Islands CampusUniversity of the West IndiesAntigua and BarbudaFive Islands Campus, University of the West Indies, Antigua and Barbuda.
| | - Cherie Tulloch
- The Cervical Cancer Elimination ProgrammeMinistry of Health, Wellness, Social Transformation and the EnvironmentAntigua and BarbudaThe Cervical Cancer Elimination Programme, Ministry of Health, Wellness, Social Transformation and the Environment, Antigua and Barbuda.
| | - Maurice McNaughton
- Centre of Excellence and InnovationMona School of Business & ManagementUniversity of the West IndiesMona CampusJamaicaCentre of Excellence and Innovation, Mona School of Business & Management, University of the West Indies, Mona Campus, Jamaica.
| | - Patrick Hosein
- Department of Computing and Information TechnologyUniversity of the West IndiesSt AugustineTrinidadDepartment of Computing and Information Technology, University of the West Indies, St Augustine, Trinidad
| | - Ian R. Hambleton
- George Alleyne Chronic Disease Research CentreCaribbean Institute for Health ResearchUniversity of the West IndiesBridgetownBarbadosGeorge Alleyne Chronic Disease Research Centre, Caribbean Institute for Health Research, University of the West Indies, Bridgetown, Barbados.
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31
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Sanchez-Martinez S, Marti-Castellote PM, Hoodbhoy Z, Bernardino G, Prats-Valero J, Aguado AM, Testa L, Piella G, Crovetto F, Snyder C, Mohsin S, Nizar A, Ahmed R, Jehan F, Jenkins K, Gratacós E, Crispi F, Chowdhury D, Hasan BS, Bijnens B. Prediction of low birth weight from fetal ultrasound and clinical characteristics: a comparative study between a low- and middle-income and a high-income country. BMJ Glob Health 2024; 9:e016088. [PMID: 39638610 PMCID: PMC11624760 DOI: 10.1136/bmjgh-2024-016088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 11/05/2024] [Indexed: 12/07/2024] Open
Abstract
INTRODUCTION Adverse perinatal outcomes (APO) pose a significant global challenge, particularly in low- and middle-income countries (LMICs). This study aims to analyse two cohorts of high-risk pregnant women for APO to comprehend risk factors and improve prediction accuracy. METHODS We considered an LMIC and a high-income country (HIC) population to derive XGBoost classifiers to predict low birth weight (LBW) from a comprehensive set of maternal and fetal characteristics including socio-demographic, past and current pregnancy information, fetal biometry and fetoplacental Doppler measurements. Data were sourced from the FeDoC (Fetal Doppler Collaborative) study (Pakistan, LMIC) and theIMPACT (Improving Mothers for a Better PrenAtal Care Trial) study (Spain, HIC), and included 520 and 746 pregnancies assessed from 28 weeks gestation, respectively. The models were trained on varying subsets of the mentioned characteristics to evaluate their contribution in predicting LBW cases. For external validation, and to highlight potential differential risk factors for LBW, we investigated the generalisation of these models across cohorts. Models' performance was evaluated through the area under the curve (AUC), and their interpretability was assessed using SHapley Additive exPlanations. RESULTS In FeDoC, Doppler variables demonstrated the highest value at predicting LBW compared with biometry and maternal clinical data (AUCDoppler, 0.67; AUCClinical, 0.65; AUCBiometry, 0.63), and its combination with maternal clinical data yielded the best prediction (AUCClinical+Doppler, 0.71). In IMPACT, fetal biometry emerged as the most predictive set (AUCBiometry, 0.75; AUCDoppler, 0.70; AUCClinical, 0.69) and its combination with Doppler and maternal clinical data achieved the highest accuracy (AUCClinical+Biometry+Doppler, 0.81). External validation consistently indicated that biometry combined with Doppler data yielded the best prediction. CONCLUSIONS Our findings provide new insights into the predictive role of different clinical and ultrasound descriptors in two populations at high risk for APO, highlighting that different approaches are required for different populations. However, Doppler data improves prediction capabilities in both settings, underscoring the value of standardising ultrasound data acquisition, as practiced in HIC, to enhance LBW prediction in LMIC. This alignment contributes to bridging the health equity gap.
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Affiliation(s)
- Sergio Sanchez-Martinez
- Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | | | - Zahra Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Gabriel Bernardino
- Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Josa Prats-Valero
- Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Ainhoa M. Aguado
- Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Lea Testa
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain
| | - Gemma Piella
- Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
| | - Francesca Crovetto
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain
- Centre for Biomedical Research on Rare Diseases (CIBER-ER), IDIBAPS, Barcelona, Spain
| | - Corey Snyder
- Cardiology Care for Children, Lancaster, Pennsylvania, USA
| | - Shazia Mohsin
- Sindh Institute of Urology and Transplantation, Karachi, Pakistan
| | - Ambreen Nizar
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Rimsha Ahmed
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Fyezah Jehan
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | - Kathy Jenkins
- Children's Hospital Boston, Boston, Massachusetts, USA
| | - Eduard Gratacós
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain
- Centre for Biomedical Research on Rare Diseases (CIBER-ER), IDIBAPS, Barcelona, Spain
- Institut de Recerca Sant Joan de Deu, Esplugues de Llobregat, Spain
| | - Fatima Crispi
- BCNatal Fetal Medicine Research Center (Hospital Clínic and Hospital Sant Joan de Déu), Universitat de Barcelona, Barcelona, Spain
- Centre for Biomedical Research on Rare Diseases (CIBER-ER), IDIBAPS, Barcelona, Spain
| | | | - Babar S Hasan
- Sindh Institute of Urology and Transplantation, Karachi, Pakistan
| | - Bart Bijnens
- Department of Engineering, Universitat Pompeu Fabra, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- ICREA, Barcelona, Spain
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Wilkinson T, Wang M, Friedman J, Gaye YE, Görgens M. Knowing when digital adds value to health: a framework for the economic evaluation of digital health interventions. OXFORD OPEN DIGITAL HEALTH 2024; 2:ii75-ii86. [PMID: 40230550 PMCID: PMC11936328 DOI: 10.1093/oodh/oqae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/02/2024] [Accepted: 08/16/2024] [Indexed: 04/16/2025]
Abstract
Digital health interventions (DHIs) hold significant promise for addressing health system challenges and the 'DHI pilot' is ubiquitous in developing-country contexts. Because the opportunity cost of investing in DHIs can be large, countries must make choices about which interventions to scale up. To make good investment decisions about DHIs, there is a need to define and establish their value within the local health system. Economic evaluation enables a systematic and evidence-based approach to describing value; however, guidance and applied economic evaluation of DHIs in developing country settings are limited. The implementation context and regulatory framework for DHIs in many resource-constrained settings is often fragmented and uncertain, creating unique challenges for economic evaluation. However, limited resources reinforce the need to adopt analytical approaches to manage this uncertainty and inform high-value investments in digital health.This paper develops an economic evaluation framework to assist in establishing the economic value of DHIs to inform policy, programming and appropriate scale-up in resource-constrained settings. It is intended for country governments and those providing technical assistance in global development related to digital health. The DHI economic evaluation framework consists of 5 steps: (1) determine the context, (2) determine the intervention type, (3) establish the level of complexity, (4) apply the analytic principles and (5) represent the value proposition. The framework facilitates methodological transparency and structure, thereby improving the overall usefulness of economic evaluations of DHIs and a starting point for more comprehensive and localized processes. RESUMEN Las Intervenciones de Salud Digital (ISD) ofrecen una promesa significativa para abordar desafíos del sistema de salud y el 'estudio piloto de ISD' es ubicuo en el contexto de los países en vías de desarrollo. Dado que el coste de oportunidad de invertir en ISD puede ser alto, los países tienen que tomar decisiones al escoger qué intervenciones escalar. Para tomar buenas decisiones en el financiamiento de las ISD, se necesita definir y establecer su valor dentro del sistema de salud local. La evaluación económica permite adscribir valía de manera sistemática y basándose en pruebas, pero la orientación y evaluación económica aplicada a ISD en países en desarrollo son escasas. El contexto para la implementación y los marcos normativos que operan sobre las ISD suelen ser inciertos y fragmentarios en lugares de limitados recursos, lo que crea desafíos singulares para la evaluación económica. A pesar de lo anterior, el hecho mismo de que los recursos sean limitados subraya la necesidad de adoptar enfoques analíticos para manejar esta incertidumbre e informar la inversión de alto nivel en salud digital.Este escrito desarrolla un marco de evaluación económica que ayude a establecer el valor económico de las ISD para informar políticas, programación, y escalamiento apropiado, en entornos de recursos limitados. Está dirigido a gobiernos de estado y a quienes proveen asistencia técnica en desarrollo global con relación a salud digital. El marco de evaluación económica de ISD consta de 5 pasos: (1) determina el contexto; (2) determina el tipo de intervención; (3) establece el nivel de complejidad; (4) aplica los principios analíticos; y (5) representa la propuesta de valor. El Marco facilita la transparencia y estructura metodológicas, mejorando así la utilidad general de las evaluaciones económicas de las ISD y brindando un punto de partida para procesos más exhaustivos y localizados. RESUMO As intervenções de saúde digitais (DHI) são muito promissoras para enfrentar os desafios do sistema de saúde e o 'piloto DHI' é omnipresente nos contextos dos países em desenvolvimento. Uma vez que o custo de oportunidade do investimento em IDS pode ser elevado, os países têm de fazer escolhas sobre quais as intervenções a alargar. Para tomar boas decisões de investimento nas IDS, é necessário definir e estabelecer o seu valor no âmbito do sistema de saúde local. A avaliação económica permite uma abordagem sistemática e baseada em provas para descrever o valor. No entanto, as orientações e a avaliação económica aplicada das IDS nos países em desenvolvimento são limitadas. O contexto de implementação e o quadro regulamentar das IDS em muitos contextos com recursos limitados são frequentemente fragmentados e incertos, criando desafios únicos para a avaliação económica. No entanto, os recursos limitados reforçam a necessidade de adotar abordagens analíticas para gerir esta incerteza e informar os investimentos de elevado valor na saúde digital.Este documento desenvolve um quadro de avaliação económica para ajudar a estabelecer o valor económico das DHI para informar a política, a programação e a expansão adequada em contextos de recursos limitados. Destina-se aos governos nacionais e aos que prestam assistência técnica no desenvolvimento global relacionado com a saúde digital. O quadro de avaliação económica das IDS é composto por 5 etapas: (1) determinar o contexto, (2) determinar o tipo de intervenção, (3) estabelecer o nível de complexidade, (4) aplicar os princípios analíticos e (5) representar a proposta de valor. O Quadro facilita a transparência e a estrutura metodológica, melhorando assim a utilidade global das avaliações económicas das IDS e constituindo um ponto de partida para processos mais abrangentes e localizados. RÉSUMÉ Les interventions de santé numérique (ISN) sont très prometteuses pour relever les défis du système de santé et le « projet pilote ISN » est omniprésent dans les contextes des pays en développement. Étant donné que le coût de l'opportunité d'investissement dans les ISN peut être important, les pays doivent faire des choix quant aux interventions à intensifier. Pour prendre de bonnes décisions d'investissement concernant les ISN, il est nécessaire de définir et d'établir leur valeur au sein du système de santé local. Une évaluation économique permet une approche systématique et fondée sur des données probantes pour décrire leur valeur, mais les directives et l'évaluation économique appliquée des ISN dans les pays en développement sont limitées. Le contexte de mise en œuvre et le cadre réglementaire des ISN dans de nombreux contextes aux ressources limitées sont souvent fragmentés et incertains, créant des défis uniques pour l'évaluation économique. Cependant, les ressources limitées renforcent la nécessité d'adopter des approches analytiques pour gérer cette incertitude et éclairer les investissements à forte valeur ajoutée dans la santé numérique.Ce document développe un cadre d'évaluation économique pour aider à établir la valeur économique des ISN afin d'éclairer les politiques, la programmation et une mise à l'échelle appropriée dans des contextes aux ressources limitées. Il est destiné aux gouvernements des pays et à ceux qui fournissent une assistance technique dans le développement mondial lié à la santé numérique. Le cadre d'évaluation économique des ISN comprend 5 étapes: (1) déterminer le contexte, (2) déterminer le type d'intervention, (3) établir le niveau de complexité, (4) appliquer les principes analytiques et (5) représenter la proposition de valeur.. Le cadre facilite la transparence et la structure méthodologiques, améliorant ainsi l'utilité globale des évaluations économiques des ISN et constituant un point de départ pour des processus plus complets et localisés.
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Affiliation(s)
- Thomas Wilkinson
- Health, Nutrition and Population Global Practice, World Bank, 1818 H Street NW, Washington DC 20433, USA
| | - Mengxiao Wang
- Health, Nutrition and Population Global Practice, World Bank, 1818 H Street NW, Washington DC 20433, USA
| | - Jed Friedman
- Development Economics Research Group, World Bank, 1818 H Street NW, Washington DC 20433, USA
| | - Yai-Ellen Gaye
- Health, Nutrition and Population Global Practice, World Bank, 1818 H Street NW, Washington DC 20433, USA
| | - Marelize Görgens
- Health, Nutrition and Population Global Practice, World Bank, 1818 H Street NW, Washington DC 20433, USA
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Krones F, Walker B. From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings. PLOS DIGITAL HEALTH 2024; 3:e0000437. [PMID: 39630646 PMCID: PMC11616830 DOI: 10.1371/journal.pdig.0000437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 10/17/2024] [Indexed: 12/07/2024]
Abstract
This article includes a literature review and a case study of artificial intelligence (AI) heart murmur detection models to analyse the opportunities and challenges in deploying AI in cardiovascular healthcare in low- or medium-income countries (LMICs). This study has two parallel components: (1) The literature review assesses the capacity of AI to aid in addressing the observed disparity in healthcare between high- and low-income countries. Reasons for the limited deployment of machine learning models are discussed, as well as model generalisation. Moreover, the literature review discusses how emerging human-centred deployment research is a promising avenue for overcoming deployment barriers. (2) A predictive AI screening model is developed and tested in a case study on heart murmur detection in rural Brazil. Our binary Bayesian ResNet model leverages overlapping log mel spectrograms of patient heart sound recordings and integrates demographic data and signal features via XGBoost to optimise performance. This is followed by a discussion of the model's limitations, its robustness, and the obstacles preventing its practical application. The difficulty with which this model, and other state-of-the-art models, generalise to out-of-distribution data is also discussed. By integrating the results of the case study with those of the literature review, the NASSS framework was applied to evaluate the key challenges in deploying AI-supported heart murmur detection in low-income settings. The research accentuates the transformative potential of AI-enabled healthcare, particularly for affordable point-of-care screening systems in low-income settings. It also emphasises the necessity of effective implementation and integration strategies to guarantee the successful deployment of these technologies.
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Affiliation(s)
- Felix Krones
- Oxford Internet Institute, University of Oxford, Oxford, United Kingdom
| | - Benjamin Walker
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
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Sadiq F, Sadiq F, Gul R, Zuhra F, Khan MK, Shah SMA, Uzma F, Khattak NUH, Alam W, Khan MU. Knowledge, Attitude, and Practice (KAP) Regarding the Use of Artificial Intelligence in Hospital Settings in Mardan, Khyber Pakhtunkhwa, Pakistan. Cureus 2024; 16:e75355. [PMID: 39781175 PMCID: PMC11707555 DOI: 10.7759/cureus.75355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/09/2024] [Indexed: 01/12/2025] Open
Abstract
Background Artificial intelligence (AI) is revolutionizing healthcare globally by enhancing diagnostic accuracy, predicting patient outcomes, and enabling personalized treatment plans. However, in low- and middle-income countries (LMICs) like Pakistan, the integration of AI into healthcare is limited due to challenges such as lack of funding, provider resistance, and inadequate training. Despite these barriers, there is growing interest among healthcare providers in understanding and adopting AI technologies to improve professional efficiency. Objective To determine knowledge, attitude, and practice (KAP) regarding the use of artificial intelligence (AI) among doctors in a hospital setting. Method This cross-sectional study was conducted at Mardan Medical Complex, Mardan, from January 2023 to March 2023. A total of 150 doctors from various departments participated by completing a validated questionnaire designed to assess their KAP regarding AI. The questionnaire, consisting of nine close-ended questions, was specifically designed and distributed to participants through social media applications, including WhatsApp (California, USA) and email, to maximize accessibility. It included structured questions rated on Likert scales to quantify the levels of knowledge, attitude, and practice. Participants were also asked about their exposure to AI-related training or professional work. Descriptive analysis was performed to determine the frequency and percentage of responses. Results The mean ± SD of age in this study was 36.95 ± 8.58 years. Male participants were 72.66%, while 27.33% were females. The response to the questions showed that the majority of participants (66%) knew the term AI; however, they were unsure regarding its use in healthcare. The majority of doctors (67.33%) had positive thoughts about the possibility of using AI in health management. Importantly, the majority of participants (72.66%) had never had a chance to do any AI-related work in their professional lives. The assessment of KAP showed that the majority of doctors had a medium level of knowledge (36.66%), a high level of attitude (57.55%), and a low level of practice (65.66%) regarding AI. Conclusions These results conclude that the knowledge regarding AI's use in healthcare is medium and its use in clinical practice is low; however, doctors have a high level of interest in applying AI to improve their professional efficiency.
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Affiliation(s)
- Faizan Sadiq
- Pediatrics Department, Mardan Medical Complex, Mardan, PAK
| | - Faran Sadiq
- Accident and Emergency, Lady Reading Hospital, Peshawar, PAK
| | - Ruba Gul
- Pediatrics Department, Khyber Teaching Hospital, Peshawar, PAK
| | - Fatima Zuhra
- Pediatrics Department, Khyber Teaching Hospital, Peshawar, PAK
| | | | | | - Faryal Uzma
- Anesthesia Department, Lady Reading Hospital, Peshawar, PAK
| | | | - Waqas Alam
- Pediatrics Department, Mardan Medical Complex, Mardan, PAK
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Lemoine É, Neves Briard J, Rioux B, Gharbi O, Podbielski R, Nauche B, Toffa D, Keezer M, Lesage F, Nguyen DK, Bou Assi E. Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review. Comput Struct Biotechnol J 2024; 24:66-86. [PMID: 38204455 PMCID: PMC10776381 DOI: 10.1016/j.csbj.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 12/05/2023] [Accepted: 12/05/2023] [Indexed: 01/12/2024] Open
Abstract
Background Computational analysis of routine electroencephalogram (rEEG) could improve the accuracy of epilepsy diagnosis. We aim to systematically assess the diagnostic performances of computed biomarkers for epilepsy in individuals undergoing rEEG. Methods We searched MEDLINE, EMBASE, EBM reviews, IEEE Explore and the grey literature for studies published between January 1961 and December 2022. We included studies reporting a computational method to diagnose epilepsy based on rEEG without relying on the identification of interictal epileptiform discharges or seizures. Diagnosis of epilepsy as per a treating physician was the reference standard. We assessed the risk of bias using an adapted QUADAS-2 tool. Results We screened 10 166 studies, and 37 were included. The sample size ranged from 8 to 192 (mean=54). The computed biomarkers were based on linear (43%), non-linear (27%), connectivity (38%), and convolutional neural networks (10%) models. The risk of bias was high or unclear in all studies, more commonly from spectrum effect and data leakage. Diagnostic accuracy ranged between 64% and 100%. We observed high methodological heterogeneity, preventing pooling of accuracy measures. Conclusion The current literature provides insufficient evidence to reliably assess the diagnostic yield of computational analysis of rEEG. Significance We provide guidelines regarding patient selection, reference standard, algorithms, and performance validation.
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Affiliation(s)
- Émile Lemoine
- Department of Neurosciences, University of Montreal, Canada
- Institute of biomedical engineering, Polytechnique Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Joel Neves Briard
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Bastien Rioux
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Oumayma Gharbi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | | | - Bénédicte Nauche
- University of Montreal Hospital Center’s Research Center, Canada
| | - Denahin Toffa
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Mark Keezer
- Department of Neurosciences, University of Montreal, Canada
- School of Public Health, University of Montreal, Canada
- Stichting Epilepsie Instellingen Nederland (SEIN), Heemstede, the Netherlands
| | - Frédéric Lesage
- Institute of biomedical engineering, Polytechnique Montreal, Canada
| | - Dang K. Nguyen
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
| | - Elie Bou Assi
- Department of Neurosciences, University of Montreal, Canada
- University of Montreal Hospital Center’s Research Center, Canada
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Chen F, Ahimaz P, Nguyen QM, Lewis R, Chung WK, Ta CN, Szigety KM, Sheppard SE, Campbell IM, Wang K, Weng C, Liu C. Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders. NPJ Digit Med 2024; 7:333. [PMID: 39572625 PMCID: PMC11582592 DOI: 10.1038/s41746-024-01331-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 11/07/2024] [Indexed: 11/24/2024] Open
Abstract
Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing (ES) or genome sequencing (GS) for conditions like congenital anomalies or developmental delays while still recommend gene panels for patients exhibiting strong manifestations of a specific disease. Recognizing the difficulty in navigating these options, we developed a machine learning model trained on 1005 patient records from Columbia University Irving Medical Center to recommend appropriate genetic tests based on the phenotype information. The model achieved a remarkable performance with an AUROC of 0.823 and AUPRC of 0.918, aligning closely with decisions made by genetic specialists, and demonstrated strong generalizability (AUROC:0.77, AUPRC: 0.816) in an external cohort, indicating its potential value for general pediatricians to expedite rare disease diagnosis by enhancing genetic test ordering.
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Affiliation(s)
- Fangyi Chen
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Priyanka Ahimaz
- Department of Pediatrics, Columbia University, New York, NY, USA
- Institute of Genomic Medicine, Columbia University, New York, NY, USA
| | - Quan M Nguyen
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Rachel Lewis
- Department of Pediatrics, Columbia University, New York, NY, USA
| | - Wendy K Chung
- Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Casey N Ta
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Katherine M Szigety
- Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sarah E Sheppard
- Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian M Campbell
- Division of Human Genetics, Department of Pediatrics, Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kai Wang
- Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
| | - Cong Liu
- Division of Genetics and Genomics, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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Castillo-Medina A, Calleja-Zardain R, Kewalramani D, Narayan M, Julio Mayol J. Inteligencia artificial como herramienta de la cirugía global en América Latina. REVISTA COLOMBIANA DE CIRUGÍA 2024. [DOI: 10.30944/20117582.2622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2025] Open
Abstract
Introducción. América Latina presenta un problema de desigualdad en el acceso a los servicios de salud en relación con el contexto sociocultural de la población, que se acentúa en relación con las actividades quirúrgicas. Ante esta situación, la cirugía global busca soluciones que permitan zanjar la brecha.
Métodos. Planteamos el uso de la inteligencia artificial (IA) como una herramienta con gran potencial para expandir el alcance de los cirujanos a las poblaciones más desatendidas de esta región.
Resultados. Las potenciales aplicaciones de la IA son innumerables. En este contexto, los recursos educacionales (chatbots) y las plataformas de telemedicina podrían acercar al profesional de la salud a donde es más necesario. Los algoritmos de seguimiento postoperatorio podrían alertarnos de factores de riesgo y posibles complicaciones. Los sistemas de análisis de información facilitarían la asignación de recursos humanos y materiales para brindar una atención más oportuna. La digitalización de las labores burocráticas y administrativas reduciría la carga para el cirujano, permitiendo dedicar este tiempo a la atención de los pacientes.
Conclusiones. Pese a que existen limitaciones, como el acceso a la tecnología, la inversión requerida y la barrera idiomática, si los gobiernos, los profesionales de la salud y los desarrolladores tecnológicos apuestan por aplicar esta herramienta en el campo de la cirugía, podríamos estar cerca de una revolución de la atención de salud.
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Alam MA, Sajib MRUZ, Rahman F, Ether S, Hanson M, Sayeed A, Akter E, Nusrat N, Islam TT, Raza S, Tanvir KM, Chisti MJ, Rahman QSU, Hossain A, Layek MA, Zaman A, Rana J, Rahman SM, Arifeen SE, Rahman AE, Ahmed A. Implications of Big Data Analytics, AI, Machine Learning, and Deep Learning in the Health Care System of Bangladesh: Scoping Review. J Med Internet Res 2024; 26:e54710. [PMID: 39466315 PMCID: PMC11555453 DOI: 10.2196/54710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/14/2024] [Accepted: 09/12/2024] [Indexed: 10/29/2024] Open
Abstract
BACKGROUND The rapid advancement of digital technologies, particularly in big data analytics (BDA), artificial intelligence (AI), machine learning (ML), and deep learning (DL), is reshaping the global health care system, including in Bangladesh. The increased adoption of these technologies in health care delivery within Bangladesh has sparked their integration into health care and public health research, resulting in a noticeable surge in related studies. However, a critical gap exists, as there is a lack of comprehensive evidence regarding the research landscape; regulatory challenges; use cases; and the application and adoption of BDA, AI, ML, and DL in the health care system of Bangladesh. This gap impedes the attainment of optimal results. As Bangladesh is a leading implementer of digital technologies, bridging this gap is urgent for the effective use of these advancing technologies. OBJECTIVE This scoping review aims to collate (1) the existing research in Bangladesh's health care system, using the aforementioned technologies and synthesizing their findings, and (2) the limitations faced by researchers in integrating the aforementioned technologies into health care research. METHODS MEDLINE (via PubMed), IEEE Xplore, Scopus, and Embase databases were searched to identify published research articles between January 1, 2000, and September 10, 2023, meeting the following inclusion criteria: (1) any study using any of the BDA, AI, ML, and DL technologies and health care and public health datasets for predicting health issues and forecasting any kind of outbreak; (2) studies primarily focusing on health care and public health issues in Bangladesh; and (3) original research articles published in peer-reviewed journals and conference proceedings written in English. RESULTS With the initial search, we identified 1653 studies. Following the inclusion and exclusion criteria and full-text review, 4.66% (77/1653) of the articles were finally included in this review. There was a substantial increase in studies over the last 5 years (2017-2023). Among the 77 studies, the majority (n=65, 84%) used ML models. A smaller proportion of studies incorporated AI (4/77, 5%), DL (7/77, 9%), and BDA (1/77, 1%) technologies. Among the reviewed articles, 52% (40/77) relied on primary data, while the remaining 48% (37/77) used secondary data. The primary research areas of focus were infectious diseases (15/77, 19%), noncommunicable diseases (23/77, 30%), child health (11/77, 14%), and mental health (9/77, 12%). CONCLUSIONS This scoping review highlights remarkable progress in leveraging BDA, AI, ML, and DL within Bangladesh's health care system. The observed surge in studies over the last 5 years underscores the increasing significance of AI and related technologies in health care research. Notably, most (65/77, 84%) studies focused on ML models, unveiling opportunities for advancements in predictive modeling. This review encapsulates the current state of technological integration and propels us into a promising era for the future of digital Bangladesh.
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Affiliation(s)
- Md Ashraful Alam
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Md Refat Uz Zaman Sajib
- Department of Health and Kinesiology, University of Illinois, Champaign and Urbana, IL, United States
| | - Fariya Rahman
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Saraban Ether
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Molly Hanson
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
| | - Abu Sayeed
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Ema Akter
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Nowrin Nusrat
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Tanjeena Tahrin Islam
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Sahar Raza
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - K M Tanvir
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Mohammod Jobayer Chisti
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Qazi Sadeq-Ur Rahman
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Akm Hossain
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - M A Layek
- Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
| | - Asaduz Zaman
- Faculty of Information Technology, Monash University, Melbourne, Australia
| | - Juwel Rana
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Research and Innovation Division, South Asian Institute for Social Transformation, Dhaka, Bangladesh
| | | | - Shams El Arifeen
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Ahmed Ehsanur Rahman
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
| | - Anisuddin Ahmed
- Maternal and Child Health Division, International Centre for Diarrheal Disease Research, Bangladesh, Dhaka, Bangladesh
- Department of Women's and Children's Health, Uppsala University, Uppsala, Sweden
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Gonzalez-Garcia A, Pérez-González S, Benavides C, Pinto-Carral A, Quiroga-Sánchez E, Marqués-Sánchez P. Impact of Artificial Intelligence-Based Technology on Nurse Management: A Systematic Review. J Nurs Manag 2024; 2024:3537964. [PMID: 40224848 PMCID: PMC11919197 DOI: 10.1155/2024/3537964] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 09/11/2024] [Accepted: 09/19/2024] [Indexed: 04/15/2025]
Abstract
Aim: To describe the use of artificial intelligence (AI) by nurse managers to enhance management, leadership, and healthcare outcomes. Background: AI represents a significant transformation in healthcare management by enhancing decision-making, communication, and resource optimization. However, the integration and strategic application of AI in nursing management are underexplored, particularly regarding its impact on leadership roles and healthcare delivery. Methods: Methodological guidelines described by PRISMA were followed, and quality was assessed using the Joanna Briggs Institute (JBI) methodology. The databases searched included the Web of Science, Scopus, CINAHLi, and PubMed. The review included quantitative, qualitative, and mixed-method studies published between January 2015 and April 2024. Results: Fourteen studies were selected for the review. The key findings indicate that AI technologies facilitate better resource management, risk assessment, and decision-making. AI also supports nurse managers in leading changes, enhancing communication, and optimizing administrative tasks. Conclusion: AI has been progressively integrated into nursing management, demonstrating significant benefits in operational efficiency, decision support, and leadership enhancement. However, challenges, such as resistance to technological change and ethical complexities, need to be addressed. Implications for Nursing Management: Specific training programs for nurse managers are essential to optimize the integration of AI. Such programs should focus on the management of AI applications and data analyses. In addition, creating interdisciplinary groups involving nurse managers, AI developers, and nursing staff is crucial for tailoring AI solutions to meet the unique needs of healthcare settings.
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Affiliation(s)
- Alberto Gonzalez-Garcia
- Faculty of Health Sciences, Nursing and Physiotherapy Department, University of Leon, León 24007, Spain
| | - Silvia Pérez-González
- Faculty of Health Sciences, Nursing and Physiotherapy Department, University of Leon, León 24007, Spain
| | - Carmen Benavides
- Department of Electric, Systems and Automatic Engineering, SALBIS Research Group, University of Leon, León 24007, Spain
| | - Arrate Pinto-Carral
- Faculty of Health Sciences, Nursing and Physiotherapy Department, SALBIS Research Group, Campus of Ponferrada, University of Leon, León 24402, Spain
| | - Enedina Quiroga-Sánchez
- Faculty of Health Sciences, Nursing and Physiotherapy Department, SALBIS Research Group, Campus of Ponferrada, University of Leon, León 24402, Spain
| | - Pilar Marqués-Sánchez
- Faculty of Health Sciences, Nursing and Physiotherapy Department, SALBIS Research Group, Campus of Ponferrada, University of Leon, León 24402, Spain
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Rapoport N, Pavelchek C, Michelson AP, Shew MA. Artificial Intelligence in Otology and Neurotology. Otolaryngol Clin North Am 2024; 57:791-802. [PMID: 38871535 DOI: 10.1016/j.otc.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2024]
Abstract
Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation.
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Affiliation(s)
- Nicholas Rapoport
- Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, PO Box 8115, St Louis, MO 63110, USA
| | - Cole Pavelchek
- Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098, USA
| | - Andrew P Michelson
- Department of Pulmonary Critical Care, Washington University School of Medicine, 660 South Euclid Avenue, PO Box 8052-43-14, St Louis, MO 63110, USA; Institute for Informatics, Washington University School of Medicine, St Louis, MO, USA
| | - Matthew A Shew
- Otology & Neurotology, Department of Otolaryngology-Head and Neck Surgery, Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, PO Box 8115, St Louis, MO 63110, USA.
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Shipton L, Vitale L. Artificial intelligence and the politics of avoidance in global health. Soc Sci Med 2024; 359:117274. [PMID: 39217716 DOI: 10.1016/j.socscimed.2024.117274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 08/05/2024] [Accepted: 08/24/2024] [Indexed: 09/04/2024]
Abstract
For decades, global health actors have centered technology in their interventions. Today, artificial intelligence (AI) is emerging as the latest technology-based solution in global health. Yet, AI, like other technological interventions, is not a comprehensive solution to the fundamental determinants of global health inequities. This article gathers and critically appraises grey and peer-reviewed literature on AI in global health to explore the question: What is avoided when global health prioritizes technological solutions to problems with deep-seated political, economic, and commercial determinants? Our literature search and selection yielded 34 documents, which we analyzed to develop seven areas where AI both continues and disrupts past legacies of technological interventions in global health, with significant implications for health equity and human rights. By focusing on the power dynamics that underpin AI's expansion in global health, we situate it as the latest in a long line of technological interventions that avoids addressing the fundamental determinants of health inequities, albeit at times differently than its technology-based predecessors. We call this phenomenon the 'politics of avoidance.' We conclude with reflections on how the literature we reviewed engages with and recognizes the politics of avoidance and with suggestions for future research, practice, and advocacy.
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Affiliation(s)
- Leah Shipton
- Department of Political Science, University of British Columbia, 1866 Main Mall C425, Vancouver, BC, V6T 1Z1, Canada; School of Public Policy, Simon Fraser University, 515 West Hasting Street Office 3269, Vancouver, BC, V6B 5K3, Canada.
| | - Lucia Vitale
- Politics Department, University of California at Santa Cruz, 639 Merrill Rd, Santa Cruz, CA, 95064, United States.
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Yang J, Dung NT, Thach PN, Phong NT, Phu VD, Phu KD, Yen LM, Thy DBX, Soltan AAS, Thwaites L, Clifton DA. Generalizability assessment of AI models across hospitals in a low-middle and high income country. Nat Commun 2024; 15:8270. [PMID: 39333515 PMCID: PMC11436917 DOI: 10.1038/s41467-024-52618-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 09/17/2024] [Indexed: 09/29/2024] Open
Abstract
The integration of artificial intelligence (AI) into healthcare systems within low-middle income countries (LMICs) has emerged as a central focus for various initiatives aiming to improve healthcare access and delivery quality. In contrast to high-income countries (HICs), which often possess the resources and infrastructure to adopt innovative healthcare technologies, LMICs confront resource limitations such as insufficient funding, outdated infrastructure, limited digital data, and a shortage of technical expertise. Consequently, many algorithms initially trained on data from non-LMIC settings are now being employed in LMIC contexts. However, the effectiveness of these systems in LMICs can be compromised when the unique local contexts and requirements are not adequately considered. In this study, we evaluate the feasibility of utilizing models developed in the United Kingdom (a HIC) within hospitals in Vietnam (a LMIC). Consequently, we present and discuss practical methodologies aimed at improving model performance, emphasizing the critical importance of tailoring solutions to the distinct healthcare systems found in LMICs. Our findings emphasize the necessity for collaborative initiatives and solutions that are sensitive to the local context in order to effectively tackle the healthcare challenges that are unique to these regions.
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Affiliation(s)
- Jenny Yang
- Department Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK.
| | | | | | | | - Vu Dinh Phu
- National Hospital for Tropical Diseases, Hanoi, Vietnam
| | | | - Lam Minh Yen
- Oxford University Clinical Research Unit, Ho Chi Minh, Vietnam
| | | | - Andrew A S Soltan
- Department Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
- Oxford Cancer & Haematology Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Department of Oncology, University of Oxford, Oxford, UK
| | - Louise Thwaites
- Oxford University Clinical Research Unit, Ho Chi Minh, Vietnam
- Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, UK
| | - David A Clifton
- Department Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
- Oxford-Suzhou Centre for Advanced Research (OSCAR), Suzhou, China
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Li Y, Cai P, Huang Y, Yu W, Liu Z, Liu P. Deep learning based detection and classification of fetal lip in ultrasound images. J Perinat Med 2024; 52:769-777. [PMID: 39028804 DOI: 10.1515/jpm-2024-0122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 07/07/2024] [Indexed: 07/21/2024]
Abstract
OBJECTIVES Fetal cleft lip is a common congenital defect. Considering the delicacy and difficulty of observing fetal lips, we have utilized deep learning technology to develop a new model aimed at quickly and accurately assessing the development of fetal lips during prenatal examinations. This model can detect ultrasound images of the fetal lips and classify them, aiming to provide a more objective prediction for the development of fetal lips. METHODS This study included 632 pregnant women in their mid-pregnancy stage, who underwent ultrasound examinations of the fetal lips, collecting both normal and abnormal fetal lip ultrasound images. To improve the accuracy of the detection and classification of fetal lips, we proposed and validated the Yolov5-ECA model. RESULTS The experimental results show that, compared with the currently popular 10 models, our model achieved the best results in the detection and classification of fetal lips. In terms of the detection of fetal lips, the mean average precision (mAP) at 0.5 and mAP at 0.5:0.95 were 0.920 and 0.630, respectively. In the classification of fetal lip ultrasound images, the accuracy reached 0.925. CONCLUSIONS The deep learning algorithm has accuracy consistent with manual evaluation in the detection and classification process of fetal lips. This automated recognition technology can provide a powerful tool for inexperienced young doctors, helping them to accurately conduct examinations and diagnoses of fetal lips.
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Affiliation(s)
- Yapeng Li
- School of Medicine, Huaqiao University, Quanzhou, China
| | - Peiya Cai
- Department of Gynecology and Obstetrics, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Yubing Huang
- Department of Ultrasound, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China
| | - Weifeng Yu
- Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Zhonghua Liu
- Department of Ultrasound, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, China
| | - Peizhong Liu
- School of Medicine, Huaqiao University, Quanzhou, China
- College of Engineering, Huaqiao University, Quanzhou, China
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Alnasser AH, Hassanain MA, Alnasser MA, Alnasser AH. Critical factors challenging the integration of AI technologies in healthcare workplaces: a stakeholder assessment. J Health Organ Manag 2024; ahead-of-print. [PMID: 39300711 DOI: 10.1108/jhom-04-2024-0135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/22/2024]
Abstract
PURPOSE This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces. DESIGN/METHODOLOGY/APPROACH The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor. FINDINGS All 46 factors were assessed as "Very Important" through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are "AI ability to learn from patient data", "insufficient data privacy measures for patients", "availability of technical support and maintenance services", "physicians' acceptance of AI in healthcare", "reliability and uptime of AI systems" and "ability to reduce medical errors". PRACTICAL IMPLICATIONS Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices. ORIGINALITY/VALUE This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders' comprehension of the factors challenging the integration of AI technologies.
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Affiliation(s)
- Abdullah H Alnasser
- Architectural Engineering and Construction Management Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Mohammad A Hassanain
- Architectural Engineering and Construction Management Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
- Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | | | - Ali H Alnasser
- Primary Healthcare Units, Al Ahsa Health Cluster, Al Ahsa, Saudi Arabia
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Schmallenbach L, Bärnighausen TW, Lerchenmueller MJ. The global geography of artificial intelligence in life science research. Nat Commun 2024; 15:7527. [PMID: 39266506 PMCID: PMC11392928 DOI: 10.1038/s41467-024-51714-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/15/2024] [Indexed: 09/14/2024] Open
Abstract
Artificial intelligence (AI) promises to transform medicine, but the geographic concentration of AI expertize may hinder its equitable application. We analyze 397,967 AI life science research publications from 2000 to 2022 and 14.5 million associated citations, creating a global atlas that distinguishes productivity (i.e., publications), quality-adjusted productivity (i.e., publications stratified by field-normalized rankings of publishing outlets), and relevance (i.e., citations). While Asia leads in total publications, Northern America and Europe contribute most of the AI research appearing in high-ranking outlets, generating up to 50% more citations than other regions. At the global level, international collaborations produce more impactful research, but have stagnated relative to national research efforts. Our findings suggest that greater integration of global expertize could help AI deliver on its promise and contribute to better global health.
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Affiliation(s)
| | - Till W Bärnighausen
- Heidelberg Institute of Global Health (HIGH), Medical School, Heidelberg University, Heidelberg, Germany
- Harvard Center for Population and Development Studies, Harvard University, Cambridge, USA
- Africa Health Research Institute (AHRI), Durban, South Africa
| | - Marc J Lerchenmueller
- University of Mannheim, Mannheim, Germany
- Leibniz Center for European Economic Research (ZEW), Mannheim, Germany
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Liao X, Yao C, Jin F, Zhang J, Liu L. Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research. BMJ Open 2024; 14:e084398. [PMID: 39260855 PMCID: PMC11409362 DOI: 10.1136/bmjopen-2024-084398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 08/26/2024] [Indexed: 09/13/2024] Open
Abstract
OBJECTIVES To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Research (CFIR) as a theoretical basis to develop strategies that promote effective implementation. DESIGN This qualitative study involved semistructured interviews with key stakeholders from both clinical settings and industry. Interview guide development, coding, analysis and reporting of findings were thoroughly informed by the updated CFIR. SETTING Four healthcare institutions in Beijing and Shanghai and two vendors of AI-assisted decision-making software for lung nodules detection and diabetic retinopathy screening were selected based on purposive sampling. PARTICIPANTS A total of 23 healthcare practitioners, 6 hospital informatics specialists, 4 hospital administrators and 7 vendors of the selected AI-assisted decision-making software were included in the study. RESULTS Within the 5 CFIR domains, 10 constructs were identified as barriers, 8 as facilitators and 3 as both barriers and facilitators. Major barriers included unsatisfactory clinical performance (Innovation); lack of collaborative network between primary and tertiary hospitals, lack of information security measures and certification (outer setting); suboptimal data quality, misalignment between software functions and goals of healthcare institutions (inner setting); unmet clinical needs (individuals). Key facilitators were strong empirical evidence of effectiveness, improved clinical efficiency (innovation); national guidelines related to AI, deployment of AI software in peer hospitals (outer setting); integration of AI software into existing hospital systems (inner setting) and involvement of clinicians (implementation process). CONCLUSIONS The study findings contributed to the ongoing exploration of AI integration in healthcare from the perspective of China, emphasising the need for a comprehensive approach considering both innovation-specific factors and the broader organisational and contextual dynamics. As China and other developing countries continue to advance in adopting AI technologies, the derived insights could further inform healthcare practitioners, industry stakeholders and policy-makers, guiding policies and practices that promote the successful implementation of imaging-based diagnostic AI-assisted decision-making software in healthcare for optimal patient care.
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Affiliation(s)
- Xiwen Liao
- Peking University First Hospital, Beijing, China
- Clinical Research Institute, Institute of Advanced Clinical Medicine, Peking University, Beijing, China
| | - Chen Yao
- Peking University First Hospital, Beijing, China
- Clinical Research Institute, Institute of Advanced Clinical Medicine, Peking University, Beijing, China
| | - Feifei Jin
- Trauma Medicine Center, Peking University People's Hospital, Beijing, China
- Key Laboratory of Trauma treatment and Neural Regeneration, Peking University, Ministry of Education, Beijing, China
| | - Jun Zhang
- MSD R&D (China) Co., Ltd, Beijing, China
| | - Larry Liu
- Merck & Co Inc, Rahway, New Jersey, USA
- Weill Cornell Medical College, New York City, New York, USA
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Nimkar P, Kanyal D, Sabale SR. Increasing Trends of Artificial Intelligence With Robotic Process Automation in Health Care: A Narrative Review. Cureus 2024; 16:e69680. [PMID: 39429258 PMCID: PMC11489308 DOI: 10.7759/cureus.69680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2024] [Accepted: 09/18/2024] [Indexed: 10/22/2024] Open
Abstract
This review explores the fast-growing importance of artificial intelligence (AI) with robotic process automation (RPA) in healthcare. AI uses intelligent algorithms to analyze data, while RPA automates repetitive tasks to improve efficiency and accuracy. These technologies are swiftly revolutionizing health care by improving diagnostic precision, accelerating administrative tasks, reducing operation timing, and improving patient care. Application of these technologies requires good technical understanding, preparedness for continuous learning, and adaptability to new challenges. This review aims to provide an in-depth study of the potential applications, present implementations, challenges, and future scope of AI with RPA in healthcare. It can provide information to researchers, professionals, and decision-makers regarding the application of the technologies under consideration for better productivity, increased security and accuracy of data, cost reduction, and personalization of healthcare provided to patients. The main results are that AI and RPA can ensure greater data security, provide supporting work in administration, like scheduling appointments and medical billing, make better decisions, enable telehealth and remote patient monitoring, reduce human error, and increase overall health outcomes. This review overviews the challenges in implementing robotics technology, focusing mainly on secondary source journals, scholarly articles, and reference books. Key findings indicate that this study reveals how robotics could alleviate healthcare professionals. Further research, investment, and collaboration will be needed to enable these technologies to reach their full potential for healthcare delivery. However, challenges such as data privacy and security concerns, high implementation costs, and regulatory and ethical considerations must be addressed. The conclusion emphasizes that while these technologies are revolutionizing healthcare by increasing efficiency and personalizing patient care, ongoing research, investment, and collaboration are essential for their successful adoption.
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Affiliation(s)
- Prashant Nimkar
- Hospital Administration, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Deepika Kanyal
- Hospital Administration, Datta Meghe Institute of Medical Sciences, Wardha, IND
| | - Shantanu R Sabale
- Hospital Administration, Jawaharlal Nehru Medical College, Wardha, IND
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Ahlin T, Sen K, Pols J. Telecare that works: lessons on integrating digital technologies in elder care from Indian transnational families. Anthropol Med 2024; 31:265-280. [PMID: 39210875 DOI: 10.1080/13648470.2024.2378726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 05/14/2024] [Accepted: 05/30/2024] [Indexed: 09/04/2024]
Abstract
In recent decades, policy makers around the world have been working on implementing various technologies into healthcare, and the Covid19 pandemic fueled this process. The specialized technological solutions for telecare - the use of technologies for care at a distance - are often adopted by users in different ways than intended, or are abandoned if the users cannot find applications that are meaningful to them. However, beyond specialized healthcare technologies, people are incorporating mundane digital technologies into their (health)care practices. In this paper, we draw on ethnographic research on the use of everyday digital technologies in Indian families where migrating children who are professional nurses care for their aging parents at a distance. Our findings show that 1) remote elder care is enacted through frequent calling which further fosters trust, necessary to provide healthcare remotely; 2) the motivation for older adults to engage with digital technologies is grounded in the value of family and affect which is consequential also for health; 3) technologies, too, require care-work in the form of everyday maintenance; and 4) in-person visits from children remain important, indicating that hybrid interaction is optimal for good care at a distance. We conclude that taking these findings into account may contribute to a more successful implementation of formal telecare systems.
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Affiliation(s)
- Tanja Ahlin
- Department of Anthropology, Amsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, The Netherlands
| | - Kasturi Sen
- Wolfson College, Oxford University, Oxford, United Kingdom
| | - Jeannette Pols
- Department of Anthropology, Amsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, The Netherlands
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Yu L, Zhai X. Use of artificial intelligence to address health disparities in low- and middle-income countries: a thematic analysis of ethical issues. Public Health 2024; 234:77-83. [PMID: 38964129 DOI: 10.1016/j.puhe.2024.05.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/26/2024] [Accepted: 05/22/2024] [Indexed: 07/06/2024]
Abstract
OBJECTIVES Artificial intelligence (AI) is reshaping health and medicine, especially through its potential to address health disparities in low- and middle-income countries (LMICs). However, there are several issues associated with the use of AI that may reduce its impact and potentially exacerbate global health disparities. This study presents the key issues in AI deployment faced by LMICs. STUDY DESIGN Thematic analysis. METHODS PubMed, Scopus, Embase and the Web of Science databases were searched, from the date of their inception until September 2023, using the terms "artificial intelligence", "LMICs", "ethic∗" and "global health". Additional searches were conducted by snowballing references before and after the primary search. The final studies were chosen based on their relevance to the topic of this article. RESULTS After reviewing 378 articles, 14 studies were included in the final analysis. A concept named the 'AI Deployment Paradox' was introduced to focus on the challenges of using AI to address health disparities in LMICs, and the following three categories were identified: (1) data poverty and contextual shifts; (2) cost-effectiveness and health equity; and (3) new technological colonisation and potential exploitation. CONCLUSIONS The relationship between global health, AI and ethical considerations is an area that requires systematic investigation. Relying on health data inherent with structural biases and deploying AI without systematic ethical considerations may exacerbate global health inequalities. Addressing these challenges requires nuanced socio-political comprehension, localised stakeholder engagement, and well-considered ethical and regulatory frameworks.
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Affiliation(s)
- Lanyi Yu
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Center for Bioethics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xiaomei Zhai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; Center for Bioethics, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
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Rudroff T, Rainio O, Klén R. Leveraging Artificial Intelligence to Optimize Transcranial Direct Current Stimulation for Long COVID Management: A Forward-Looking Perspective. Brain Sci 2024; 14:831. [PMID: 39199522 PMCID: PMC11353063 DOI: 10.3390/brainsci14080831] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 08/12/2024] [Accepted: 08/18/2024] [Indexed: 09/01/2024] Open
Abstract
Long COVID (Coronavirus disease), affecting millions globally, presents unprecedented challenges to healthcare systems due to its complex, multifaceted nature and the lack of effective treatments. This perspective review explores the potential of artificial intelligence (AI)-guided transcranial direct current stimulation (tDCS) as an innovative approach to address the urgent need for effective Long COVID management. The authors examine how AI could optimize tDCS protocols, enhance clinical trial design, and facilitate personalized treatment for the heterogeneous manifestations of Long COVID. Key areas discussed include AI-driven personalization of tDCS parameters based on individual patient characteristics and real-time symptom fluctuations, the use of machine learning for patient stratification, and the development of more sensitive outcome measures in clinical trials. This perspective addresses ethical considerations surrounding data privacy, algorithmic bias, and equitable access to AI-enhanced treatments. It also explores challenges and opportunities for implementing AI-guided tDCS across diverse healthcare settings globally. Future research directions are outlined, including the need for large-scale validation studies and investigations of long-term efficacy and safety. The authors argue that while AI-guided tDCS shows promise for addressing the complex nature of Long COVID, significant technical, ethical, and practical challenges remain. They emphasize the importance of interdisciplinary collaboration, patient-centered approaches, and a commitment to global health equity in realizing the potential of this technology. This perspective article provides a roadmap for researchers, clinicians, and policymakers involved in developing and implementing AI-guided neuromodulation therapies for Long COVID and potentially other neurological and psychiatric conditions.
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Affiliation(s)
- Thorsten Rudroff
- Turku PET Centre, University of Turku, Turku University Hospital, 20520 Turku, Finland; (O.R.); (R.K.)
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