There is a growing body of clinical research on the utility of synthetic data derivatives, an emerging research tool in medicine. In nephrology, clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy. This is especially important given the epidemiology of chronic kidney disease, renal oncology, and hypertension worldwide. However, there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.

Artificial intelligence (AI) has shown immense potential in the field of medicine, but its actual effectiveness and safety still need to be validated through clinical trials. Currently, the research themes, methodologies, and development trends of AI-related clinical trials remain unclear, and further exploration of these studies will be crucial for uncovering AI's practical application potential and promoting its broader adoption in clinical settings.

To analyze the current status, hotspots, and trends of published clinical research on AI applications.

Publications related to AI clinical applications were retrieved from the Web of Science database. Relevant data were extracted using VOSviewer 1.6.17 to generate visual cooperation network maps for countries, organizations, authors, and keywords. Burst citation detection for keywords and citations was performed using CiteSpace 5.8.R3 to identify sudden surges in citation frequency within a short period, and the theme evolution was analyzed using SciMAT to track the development and trends of research topics over time.

A total of 22,583 articles were obtained from the Web of Science database. Seven-hundred and thirty-five AI clinical application research were published by 1764 institutions from 53 countries. The majority of publications were contributed by the United States, China, and the UK. Active collaborations were noted among leading authors, particularly those from developed countries. The publications mainly focused on evaluating the application value of AI technology in the fields of disease diagnosis and classification, disease risk prediction and management, assisted surgery, and rehabilitation. Deep learning and chatbot technologies were identified as emerging research hotspots in recent studies on AI applications.

A total of 735 articles on AI in clinical research were analyzed, with publication volume and citation counts steadily increasing each year. Institutions and researchers from the United States contributed the most to the research output in this field. Key areas of focus included AI applications in surgery, rehabilitation, disease diagnosis, risk prediction, and health management, with emerging trends in deep learning and chatbots. This study also provides detailed and intuitive information about important articles, journals, core authors, institutions, and topics in the field through visualization maps, which will help researchers quickly understand the current status, hotspots, and trends of artificial intelligence clinical application research. Future clinical trials of artificial intelligence should strengthen scientific design, ethical compliance, and interdisciplinary and international cooperation and pay more attention to its practical clinical value and reliable application in diverse scenarios.

This bibliometric analysis aims to explore scientific data on Artificial Intelligence (AI) and Head and Neck Cancer (HNC).

AI-related HNC articles from the Web of Science Core Collection were searched. VosViewer and Biblioshiny/Bibiometrix for R Studio were used for data synthesis. This analysis covered key characteristics such as sources, authors, affiliations, countries, citations and top cited articles, keyword analysis, and trending topics.

A total of 1,019 papers from 1995 to 2024 were included. Among them, 71.6% were original research articles, 7.6% were reviews, and 20.8% took other forms. The fifty most cited documents highlighted radiology as the most explored specialty, with an emphasis on deep learning models for segmentation. The publications have been increasing, with an annual growth rate of 94.4% after 2016. Among the 20 most productive countries, 14 are high-income economies. The keywords of strong citation revealed 2 main clusters: radiomics and radiotherapy. The most frequently keywords include machine learning, deep learning, artificial intelligence, and head and neck cancer, with recent emphasis on diagnosis, survival prediction, and histopathology.

There has been an increase in the use of AI in HNC research since 2016 and indicated a notable disparity in publication quantity between high-income and low/middle-income countries. Future research should prioritize clinical validation and standardization to facilitate the integration of AI in HNC management, particularly in underrepresented regions.

Chronic disease monitoring programs often adopt a one-size-fits-all approach that does not consider variation in need, potentially leading to excessive or insufficient support for patients at different risk levels. Machine learning (ML) developments offer new opportunities for personalised medicine in clinical practice.

To demonstrate the potential of ML to guide resource allocation and tailored disease management, this study aims to predict the optimal testing interval for monitoring blood glucose (HbA1c) for patients with Type 2 Diabetes (T2D). We examine fairness across income and education levels and evaluate the risk of false-positives and false-negatives.

Danish administrative registers are linked with national clinical databases. Our population consists of all T2D patients from 2015-2018, a sample of more than 57,000. Data contains patient-level clinical measures, healthcare utilisation, medicine, and socio-demographics.

We classify HbA1c test intervals into four categories (3, 6, 9, and 12 months) using three classification algorithms: logistic regression, random forest, and extreme gradient boosting (XGBoost). Feature importance is assessed with SHAP model explanations on the best-performing model, which was XGBoost. A training set comprising 80% of the data is used to predict optimal test intervals, with 20% reserved for testing. Cross-validation is employed to enhance the model's reliability and reduce overfitting. Model performance is evaluated using ROC-AUC, and optimal intervals are determined based on a "time-to-next-positive-test" concept, with different durations associated with specific intervals.

The model exhibits varying predictive accuracy, with AUC scores ranging from 0.53 to 0.89 across different test intervals. We find significant potential to free resources by prolonging the test interval for well-controlled patients. The fairness metric suggests models perform well in terms of equality. There is a sizeable risk of false negatives (predicting longer intervals than optimal), which requires attention.

We demonstrate the potential to use ML in personalised diabetes management by assisting physicians in categorising patients by testing frequencies. Clinical validation on diverse patient populations is needed to assess the model's performance in real-world settings.

Recent years have witnessed an explosive surge in dental research related to artificial intelligence (AI). These applications have optimised dental workflows, demonstrating significant clinical importance. Understanding the current landscape and trends of this topic is crucial for both clinicians and researchers to utilise and advance this technology. However, a comprehensive scientometric study regarding this field had yet to be performed.

A literature search was conducted in the Web of Science Core Collection database to identify eligible "research articles" and "reviews." Literature screening and exclusion were performed by 2 investigators. Thereafter, VOSviewer was utilised in co-occurrence analysis and CiteSpace in co-citation analysis. R package Bibliometrix was employed to automatically calculate scientific impacts, determining the core authors and journals. Altmetric data were described narratively and supplemented with Spearman correlation analysis.

A total of 1558 research publications were included. During the past 5 years, AI-related dental publications drastically increased in number, from 36 to 581. Diagnostics and Scientific Reports published the most articles, whereas Journal of Dental Research received the highest number of citations per article. China, the US, and South Korea emerged as the most prolific countries, whilst Germany received the highest number of citations per article (23.29). Charité Universitätsmedizin Berlin was the institution with the highest number of publications and citations per article (29.16). Altmetric Attention Score was correlated with News Mentions (P < .001), and significant associations were observed amongst Dimension Citations, Mendeley Readers, and Web of Science Citations (P < .001).

The publication numbers regarding AI-related dental research have been rising rapidly and may continue their upwards trend. China, the US, South Korea, and Germany had promoted the progress of AI-related dental research. Disease diagnosis, orthodontic applications, and morphology segmentation were current hotspots. Attention mechanism, explainable AI, multimodal data fusion, and AI-generated text assistants necessitate future research and exploration.

To conduct a comprehensive bibliometric analysis of the literature on artificial intelligence (AI) applications in orthodontics to provide a detailed overview of the current research trends, influential works, and future directions.

A research strategy in The Web of Science Core Collection has been conducted to identify original articles regarding the use of AI in orthodontics. Articles were screened and selected by two independent reviewers and the following data were imported and processed for analysis: rankings, centrality metrics, publication trends, co-occurrence and clustering of keywords, journals, articles, authors, nations, and organizations. Data were analyzed using CiteSpace 6.3.R2 and VOSviewer.

Almost 83% of the 381 chosen articles were released in the last three and a half years. Studies were published either in highly impacted orthodontic journals and also in journals related to informatics engineering, computer science, and medical imaging. Two-thirds of the available literature originated from China, the USA, and South Korea. AI-driven cephalometric landmarking and automatic segmentation were the main areas of research.

This report offers a thorough overview of the AI current trend in orthodontics and it highlights prominent research areas focused on increasing the speed and efficiency of orthodontic care. Furthermore, it offers insight into potential directions for future research.

Collaborative research efforts will be necessary to strengthen the maturity and robustness of AI models and to make AI-based clinical research sufficiently reliable for routine orthodontic clinical practice.

Over the past decade, Deep Learning (DL) techniques have demonstrated remarkable advancements across various domains, driving their widespread adoption. Particularly in medical image analysis, DL received greater attention for tasks like image segmentation, object detection, and classification. This paper provides an overview of DL-based object recognition in medical images, exploring recent methods and emphasizing different imaging techniques and anatomical applications. Utilizing a meticulous quantitative and qualitative analysis following PRISMA guidelines, we examined publications based on citation rates to explore into the utilization of DL-based object detectors across imaging modalities and anatomical domains. Our findings reveal a consistent rise in the utilization of DL-based object detection models, indicating unexploited potential in medical image analysis. Predominantly within Medicine and Computer Science domains, research in this area is most active in the US, China, and Japan. Notably, DL-based object detection methods have gotten significant interest across diverse medical imaging modalities and anatomical domains. These methods have been applied to a range of techniques including CR scans, pathology images, and endoscopic imaging, showcasing their adaptability. Moreover, diverse anatomical applications, particularly in digital pathology and microscopy, have been explored. The analysis underscores the presence of varied datasets, often with significant discrepancies in size, with a notable percentage being labeled as private or internal, and with prospective studies in this field remaining scarce. Our review of existing trends in DL-based object detection in medical images offers insights for future research directions. The continuous evolution of DL algorithms highlighted in the literature underscores the dynamic nature of this field, emphasizing the need for ongoing research and fitted optimization for specific applications.

To provide a comprehensive and updated mapping of observational studies assessing the relationship between periodontitis and systemic diseases through a bibliometric and visual analysis. A search was conducted using the Web of Science database, covering the period 1989 to 2024. The Medical Subject Headings (MeSH) from the US National Library of Medicine was used to categorize systemic conditions, focusing on terms unrelated to stomatognathic diseases. The analysis included keyword co-occurrence mapping, co-authorship, bibliographic coupling, and co-citation analysis. Quality indicators such as silhouette score, modularity, and centrality were considered to assess the network's quality. The research strategy identified 6106 records, of which 1519 met the inclusion criteria. The analysis revealed that 46.73% of the literature on the topic was published in the last 5 years and that the annual publication trend peaked in 2023. Nutritional & Metabolic Diseases (n = 398), Cardiovascular Diseases (n = 335), Female Urogenital Diseases & Pregnancy Complications (n = 244), and Musculoskeletal Diseases (n = 182) were the most representative categories of systemic diseases associated with periodontitis. The most co-cited journals on the topic were the Journal of Periodontology (n = 1412), the Journal of Clinical Periodontology (n = 1343), the Journal of Dental Research (n = 940), and Periodontology 2000 (n = 849). The USA, China, Brazil, and Sweden were the countries that contributed the most to the number of publications. The analysis conducted in the present study revealed a growing trend of observational studies evaluating the association between periodontitis and systemic diseases, highlighting the negative impact of periodontitis on a plethora of systemic conditions and a rising translational interest in this relationship. With an aging population, periodontitis is expected to affect a growing number of people in the coming decades, presenting significant challenges to public health. Improved knowledge is, therefore, essential to enable more comprehensive care, preventive strategies, and optimal oral health for patients with periodontitis.

Skin injuries caused by physical, pathological, and chemical factors not only compromise appearance and barrier function but can also lead to life-threatening microbial infections, posing significant challenges for patients and healthcare systems. Artificial intelligence (AI) technology has demonstrated substantial advantages in processing and analyzing image information. Recently, AI-based methods and algorithms, including machine learning, deep learning, and neural networks, have been extensively explored in wound care and research, providing effective clinical decision support for wound diagnosis, treatment, prognosis, and rehabilitation. However, challenges remain in achieving a closed-loop care system for the comprehensive application of AI in wound management, encompassing wound diagnosis, monitoring, and treatment. This review comprehensively summarizes recent advancements in AI applications in wound repair. Specifically, it discusses AI's role in injury type classification, wound measurement (including area and depth), wound tissue type classification, wound monitoring and prediction, and personalized treatment. Additionally, the review addresses the challenges and limitations AI faces in wound management. Finally, recommendations for the application of AI in wound repair are proposed, along with an outlook on future research directions, aiming to provide scientific evidence and technological support for further advancements in AI-driven wound repair theranostics.

Bibliometric studies of scientific production in Spanish Rheumatology are scarce. The aim of this study was to analyze the bibliographic production of Rheumatology Services in Spain over the period 2013-2022.

Original articles and reviews with the affiliation of the first or corresponding author to a Spanish rheumatology Service/Department/Section/Unit were identified in the Web of Science Core Collection and Scopus databases. Keywords and titles were used to classify articles by field (clinical, epidemiological or basic) and pathology. International collaborations and collaborations between Autonomous Communities (AC) were identified. Quantitative bibliometric indicators were obtained (number of articles published per year, pathology and AC) and impact indicators were obtained (based on the number of citations per article). The H-index was also calculated.

The total number of publications was 2321, with an annual growth rate of 4.1% in the period analyzed. In 14.1% of the articles there were international collaborations, mainly with the United States and the United Kingdom, while between ACs the most numerous were between Madrid and Catalonia. The pathologies with the highest H-index were rheumatoid arthritis (RA), spondyloarthropathies (SpA), osteoarthritis and vasculitis (34, 32, 28 and 26, respectively). The H-index for the Spanish rheumatology services as a whole was 69.

The scientific production of rheumatology services/departments/sections/units in our country increased between 2013 and 2022. By pathology, the scientific production in RA and SpA stands out.

This study aimed to evaluate the related research on artificial intelligence (AI) in inflammatory bowel disease (IBD) through bibliometrics analysis and identified the research basis, current hotspots, and future development.

The related literature was acquired from the Web of Science Core Collection (WoSCC) on 31 December 2024. Co-occurrence and cooperation relationship analysis of (cited) authors, institutions, countries, cited journals, references, and keywords in the literature were carried out through CiteSpace 6.1.R6 software and the Online Analysis platform of Literature Metrology. Meanwhile, relevant knowledge maps were drawn, and keywords clustering analysis was performed.

According to WoSCC, 1919 authors, 790 research institutions, 184 journals, and 49 countries/regions published 176 AI-related papers in IBD during 1999-2024. The number of papers published has increased significantly since 2019, reaching a maximum by 2023. The United States had the highest number of publications and the closest collaboration with other countries. The clustering analysis showed that the earliest studies focused on "psychometric value" and then moved to "deep learning model," "intestinal ultrasound," and "new diagnostic strategies."

This study is the first bibliometric analysis to summarize the current status and to visually reveal the development trends and future research hotspots of the application of AI in IBD. The application of AI in IBD is still in its infancy, and the focus of this field will shift to improving the efficiency of diagnosis and treatment through deep learning techniques, big data-based treatment, and prognosis prediction.

The application of artificial intelligence (AI)-based clinical decision support systems (CDSS) in the healthcare domain is still limited. End-users' difficulty understanding how the outputs of opaque black AI models are generated contributes to this. It is still unknown which explanations are best presented to end users and how to design the interfaces they are presented in (explanation user interface, XUI). This article aims to provide an overview of recommendations for the user-centered design of XUIs from the scientific literature.

A scoping review was conducted to identify recommendations for the design of XUIs. Articles published between 2017 and 2022 in English or German, presenting original research or literature reviews, focusing on XUIs for end users or domain experts, which are intended for presentation in graphical user interfaces and from which recommendations could be extracted were included in the review. Articles were retrieved from Scopus, Web of Science, IEEE Explore, PubMed, ACM Digital Library, and PsychInfo. A mind map was created to organize and summarize the identified recommendations.

From the 47 included articles, 240 recommendations for the user-centered design were extracted. The organization in a mind map resulted in 64 summarized recommendations.

This review provides a synopsis of basic recommendations for the user-centered design of XUIs, focusing on the healthcare domain. During the analysis of the articles, it became clear that no specific and directly implementable design recommendations for AI-based CDSS can be given, but only basic recommendations for raising awareness about the user-centered design of XUIs.

The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI-integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms.

The scoping review followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines.

A structured search was performed across seven databases according to PRISMA-ScR: PubMed, EMBASE, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI).

Twenty-six articles published between 2019 and 2023 met the inclusion and exclusion criteria, and 46% of studies were conducted in the US. The average MERSQI score was 12.2, indicative of moderate- to high-quality studies. The most used ML algorithm was Random Forest. The four second-most used were logistic regression, least absolute shrinkage and selection operator, decision tree, and support vector machine. Most ML models were evaluated by calculating sensitivity (recall)/specificity, accuracy, receiver operating characteristic (ROC), area under the ROC (AUROC), and positive/negative prediction value (precision). Half of the studies focused on nursing staff or students and hospital readmission or emergency department visits. Only 11 articles reported the most effective ML algorithm(s).

The scoping review provides insights into the current status of ML research in nursing and recognition of its significance in nursing research, confirming the benefits of ML in healthcare. Recommendations include incorporating experimental designs in research studies to optimize the use of ML models across various nursing domains.

The scoping review demonstrates substantial clinical relevance of ML applications for nurses, nurse practitioners, administrators, and researchers. The integration of ML into healthcare systems and its impact on nursing practices have important implications for patient care, resource management, and the evolution of nursing research.

The Alliance for Healthier Communities is a learning health system that supports Community Health Centres (CHCs) across Ontario, Canada to provide team-based primary health care to people who otherwise experience barriers to care. This case study describes the ongoing process and lessons learned from the first Alliance for Healthier Communities' Practice Based Learning Network (PBLN) data-driven decision support tool co-development project.

We employ an iterative approach to problem identification and methods development for the decision support tool, moving between discussion sessions and case studies with CHC electronic health record (EHR) data. We summarize our work to date in terms of six stages: population-level descriptive-exploratory study, PBLN team engagement, decision support tool problem selection, sandbox case study 1: individual-level risk predictions, sandbox case study 2: population-level planning predictions, project recap and next steps decision.

The population-level study provided an initial point of engagement to consider how clients are (not) represented in EHR data and to inform problem selection and methodological decisions thereafter. We identified three initial meaningful types of decision support, with target application areas: risk prediction/screening, triaging specialized program referrals, and identifying care access needs. Based on feasibility and expected impact, we started with the goal to support earlier identification of mental health decline after diabetes diagnosis. As discussions deepened around clinical use cases associated with example prediction task set ups, the target problem evolved towards supporting the upstream task of organizational planning and advocacy for adequate mental health care service capacity to meet incoming needs.

This case study contributes towards a tool to support diabetes and mental health care, as well as lays groundwork for future CHC EHR-based decision support tool initiatives. We share lessons learned and reflections from our process that other primary health care organizations may use to inform their own co-development initiatives while we continue to work on advancing the population-level capacity planning model.

Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of machine learning (ML) tools.

The aim of this systematic review was to assess the performance as well as the methodological quality of validated ML models for the prediction of SSIs.

A systematic search in PubMed, Embase and the Cochrane library was performed from inception until July 2023. Exclusion criteria were the absence of reported model validation, SSIs as part of a composite adverse outcome, and pediatric populations. ML performance measures were evaluated, and ML performances were compared to regression-based methods for studies that reported both methods. Risk of bias (ROB) of the studies was assessed using the Prediction model Risk of Bias Assessment Tool.

Of the 4,377 studies screened, 24 were included in this review, describing 85 ML models. Most models were only internally validated (81%). The C-statistic was the most used performance measure (reported in 96% of the studies) and only two studies reported calibration metrics. A total of 116 different predictors were described, of which age, steroid use, sex, diabetes, and smoking were most frequently (100% to 75%) incorporated. Thirteen studies compared ML models to regression-based models and showed a similar performance of both modelling methods. For all included studies, the overall ROB was high or unclear.

A multitude of ML models for the prediction of SSIs are available, with large variability in performance. However, most models lacked external validation, performance was reported limitedly, and the risk of bias was high. In studies describing both ML models and regression-based models, one modelling method did not outperform the other.

Studies on the application and exploration of human-computer interaction (HCI) technologies within the healthcare sector have rapidly expanded, showcasing the immense potential of HCI to enhance medical services, elevate patient experiences, and advance health management. Despite this proliferating interest, there is a notable shortage of comprehensive bibliometric analyses dedicated to the application of HCI in healthcare, which limits a thorough comprehension of the growth trends and future trajectories in this area.

To bridge this gap, we employed bibliometric methods using the CiteSpace tool to systematically review and analyze the current state and trends of HCI research in healthcare. A meticulous topic search of Web of Science yielded 3598 papers published between 2004 and 2023.

Through literature analysis, the most productive researchers, institutes, and countries/territories and the collaboration networks among authors and countries within the field were analyzed. Additionally, by conducting a co-citation analysis, journals and literature with high citation rates and influence within the academic community in this field were revealed. Through a cluster analysis based on literature co-citations and keyword burst analyses, we further explored the main research themes and hot topics within the fields of healthcare and HCI.

In summary, through a comprehensive and systematic bibliometric analysis, this study provides a solid knowledge foundation for HCI in the healthcare research community, thereby fostering the development of innovative research and the optimization of practical applications in the field.

Background: This research employs bibliometric and text-mining analysis to explore artificial intelligence (AI) advancements within surgical procedures. The growing significance of AI in healthcare underscores the need for healthcare managers to prioritize investments in this technology. Purpose: To assess the increasing impact of AI on surgical practices through a comprehensive analysis of scientific literature, providing insights that can guide managerial decision-making in adopting AI solutions.Research Design: The study analyzes over 6000 scientific articles published since 1990 to evaluate trends and contributions in the field, informing managers about the current landscape of AI in surgery.Study Sample: The research focuses on publications from various influential publishers across North America, Northern Asia, and Eastern & Western Europe, highlighting key markets for AI implementation in surgical settings.Data Collection and Analysis: A bibliometric approach was utilized to identify key contributors and influential journals. At the same time, text-mining techniques highlighted significant keywords related to AI in surgery, aiding managers in recognizing essential areas for further exploration and investment.Results: The year 2022 marked a significant upsurge in publications, indicating widespread AI integration in healthcare. The U.S. emerged as the foremost contributor, followed by China, the UK, Germany, Italy, the Netherlands, and India. Key journals, such as Annals of Surgery and Spine Journal, play a crucial role in disseminating research findings, serving as valuable resources for managers seeking to stay informed.Conclusions: The findings underscore AI's pivotal role in enhancing diagnostic precision, predicting treatment outcomes, and improving operational efficiency in surgical practices. This progress represents a significant milestone in modern medical science, paving the way for intelligent healthcare solutions and further advancements in the field. Healthcare managers should leverage these insights to foster innovation and improve patient care standards.

This survey aims to understand frontline healthcare professionals' perceptions of artificial intelligence (AI) in healthcare and assess how AI familiarity influences these perceptions.

We conducted a survey from February to March 2023 of physicians and physician assistants registered with the Kansas State Board of Healing Arts. Participants rated their perceptions toward AI-related domains and constructs on a 5-point Likert scale, with higher scores indicating stronger agreement. Two sub-groups were created for analysis to assess the impact of participants' familiarity and experience with AI on the survey results.

From 532 respondents, key concerns were Perceived Communication Barriers (median = 4.0, IQR = 2.8-4.8), Unregulated Standards (median = 4.0, IQR = 3.6-4.8), and Liability Issues (median = 4.0, IQR = 3.5-4.8). Lower levels of agreement were noted for Trust in AI Mechanisms (median = 3.0, IQR = 2.2-3.4), Perceived Risks of AI (median = 3.2, IQR = 2.6-4.0), and Privacy Concerns (median = 3.3, IQR = 2.3-4.0). Positive correlations existed between Intention to use AI and Perceived Benefits (r = 0.825) and Trust in AI Mechanisms (r = 0.777). Perceived risk negatively correlated with Intention to Use AI (r = -0.718). There was no difference in perceptions between AI experienced and AI naïve subgroups.

The findings suggest that perceptions of benefits, trust, risks, communication barriers, regulation, and liability issues influence healthcare professionals' intention to use AI, regardless of their AI familiarity.

The study highlights key factors affecting AI adoption in healthcare from the frontline healthcare professionals' perspective. These insights can guide strategies for successful AI implementation in healthcare.

There is a growing enthusiasm for machine learning (ML) among academics and health care practitioners. Despite the transformative potential of ML-based applications for patient care, their uptake and implementation in health care organizations are sporadic. Numerous challenges currently impede or delay the widespread implementation of ML in clinical practice, and limited knowledge is available regarding how these challenges have been addressed.

This work aimed to (1) examine the characteristics of ML-based applications and the implementation process in clinical practice, using the Consolidated Framework for Implementation Research (CFIR) for theoretical guidance and (2) synthesize the strategies adopted by health care organizations to foster successful implementation of ML.

A systematic literature review was conducted based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The search was conducted in PubMed, Scopus, and Web of Science over a 10-year period (2013-2023). The search strategy was built around 4 blocks of keywords (artificial intelligence, implementation, health care, and study type). Only empirical studies documenting the implementation of ML applications in clinical settings were considered. The implementation process was investigated using a thematic analysis and coding procedure.

Thirty-four studies were selected for data synthesis. Selected papers were relatively recent, with only 9% (3/34) of records published before 2019. ML-based applications were implemented mostly within hospitals (29/34, 85%). In terms of clinical workflow, ML-based applications supported mostly prognosis (20/34, 59%) and diagnosis (10/34, 29%). The implementation efforts were analyzed using CFIR domains. As for the inner setting domain, access to knowledge and information (12/34, 35%), information technology infrastructure (11/34, 32%), and organizational culture (9/34, 26%) were among the most observed dimensions influencing the success of implementation. As for the ML innovation itself, factors deemed relevant were its design (15/34, 44%), the relative advantage with respect to existing clinical practice (14/34, 41%), and perceived complexity (14/34, 41%). As for the other domains (ie, processes, roles, and outer setting), stakeholder engagement (12/34, 35%), reflecting and evaluating practices (11/34, 32%), and the presence of implementation leaders (9/34, 26%) were the main factors identified as important.

This review sheds some light on the factors that are relevant and that should be accounted for in the implementation process of ML-based applications in health care. While the relevance of ML-specific dimensions, like trust, emerges clearly across several implementation domains, the evidence from this review highlighted that relevant implementation factors are not necessarily specific for ML but rather transversal for digital health technologies. More research is needed to further clarify the factors that are relevant to implementing ML-based applications at the organizational level and to support their uptake within health care organizations.

PROSPERO 403873; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=403873.

RR2-10.2196/47971.

The integration of big data with artificial intelligence in the field of digital health has brought a new dimension to healthcare service delivery. AI technologies that provide value by using big data obtained in the provision of health services are being added to each passing day. There are also some problems related to the use of AI technologies in health service delivery. In this respect, it is aimed to understand the use of digital health, AI and big data technologies in healthcare services and to analyze the developments and trends in the sector.

In this research, 191 studies published between 2016 and 2023 on digital health, AI and its sub-branches and big data were analyzed using VOSviewer and Rstudio Bibliometrix programs for bibliometric analysis. We summarized the type, year, countries, journals and categories of publications; matched the most cited publications and authors; explored scientific collaborative relationships between authors and determined the evolution of research over the years through keyword analysis and factor analysis of publications. The content of the publications is briefly summarized.

The data obtained showed that significant progress has been made in studies on the use of AI technologies and big data in the field of health, but research in the field is still ongoing and has not yet reached saturation.

Although the bibliometric analysis study conducted has comprehensively covered the literature, a single database has been utilized and limited to some keywords in order to reach the most appropriate publications on the subject.

The analysis has addressed important issues regarding the use of developing digital technologies in health services and is thought to form a basis for future researchers.

In today's world, where significant developments are taking place in the field of health, it is necessary to closely follow the development of digital technologies in the health sector and analyze the current situation in order to guide both stakeholders and those who will work in this field.

As artificial intelligence (AI) takes an increasingly prominent role in health care, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study aims to provide a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research.

We conducted a bibliometric analysis of AI-based research in the discipline of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. Based on the bibliometric data, we examined publication trends and patterns, characteristics of research activities, and research hotspot tendencies.

From 1985 onwards, there has been a consistent annual increase in publications, averaging 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, aphasia, dysarthria, Parkinson's disease, and Alzheimer's disease. Noteworthy AI models instantiated in CSD research encompass support vector machine, convolutional neural network, and hidden Markov model, among others.

Compared to AI applications in other fields, the adoption of AI in CSD has lagged slightly behind. While CSD studies primarily use classical machine learning techniques, there is a growing trend toward the integration of deep learning methods. AI technology offers significant benefits for both research and clinical practice in CSD, but it also presents certain challenges. Moving forward, collaboration among technological, research, and clinical domains is essential to empower researchers and speech-language pathologists to effectively leverage AI technology for the study, diagnosis, assessment, and rehabilitation of CSD.

https://doi.org/10.23641/asha.27162564.

This research aimed to examine the global developing patterns in the treatment of diabetic peripheral neuropathy (DPN) using a bibliometric analysis of published literature.

We extracted publication data from papers published between 2014 and 2024 using a specific topic search in the "Web of Science Core Collection" (WoSCC) database. Various metrics, such as the number of papers, citations, authors, countries, institutions, and references, were collected for analysis. To further explore the data, CiteSpace was employed to examine co-citation patterns among authors, identify collaborative efforts between countries and institutions, and uncover emerging trends using burst keywords and reference analysis.

The study encompassed 2,488 publications that exhibited an increasing trend in annual output. Notably, the journal PAIN, the United States, the Pfizer institution, and the author Feldman, EvaL emerged as the most prolific contributors to this research domain. The term "placebo-controlled trial" was the most prominent burst keyword from 2014 to 2017, whereas "spinal cord stimulation" held this distinction in the recent 5-year span. Furthermore, the publication titled "Pharmacotherapy for neuropathic pain in adults: a systematic review and meta-analysis-2015" demonstrated the highest burst in terms of references.

This study is the first to objectively reveal the current hotspots and trends in DPN treatment. The results indicate that drug therapy remains the primary first-line treatment for DPN and that future research on DPN treatment will likely focus on "spinal cord stimulation" and "pain management." These findings provide valuable insights into DPN treatment.

Moral courage is a recognized virtue. Researchers have focused on various aspects of nursing moral courage, such as its conceptualization and influencing factors. Within these studies, various literature reviews have been conducted, but to our knowledge, bibliometric mapping has not been utilized.

This article aims to analyze the production of literature within nursing moral courage research.

To investigate publication patterns, we employed VOSviewer and CiteSpace software, focusing on publication dynamics, prolific research entities, and most cited articles. Additionally, we forecasted future research trends.

In our study, ethical review was not required.

A total of 105 information sources were identified in the WoS database. Overall, there has been a significant increase in research volume after 2020. The most prolific countries are the United States, Finland, and China, while the most prolific source title is "Nursing Ethics." Keywords are also related to moral dilemmas and ethics. However, there are further improvements needed in international cooperation.

The results proposed in this paper can serve as a starting point for comprehensive or systematic literature reviews and seek more detailed data, information, and knowledge in the field of nursing moral courage. It can enable outsiders to quickly understand research on nursing moral courage, whether for in-depth exploration or simply to facilitate more effective collaboration with nursing ethics experts.

Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field.

We performed a bibliometric analysis of the American Journal of Neuroradiology; the journal was queried for original research articles published since inception (January 1, 1980) to December 3, 2022 that contained any of the following key terms: "machine learning," "artificial intelligence," "radiomics," "deep learning," "neural network," "generative adversarial network," "object detection," or "natural language processing." Articles were screened by 2 independent reviewers, and categorized into statistical modeling (type 1), AI/ML development (type 2), both representing developmental research work but without a direct clinical integration, or end-user application (type 3), which is the closest surrogate of potential AI/ML integration into day-to-day practice. To better understand the limiting factors to type 3 articles being published, we analyzed type 2 articles as they should represent the precursor work leading to type 3.

A total of 182 articles were identified with 79% being nonintegration focused (type 1 n = 53, type 2 n = 90) and 21% (n = 39) being type 3. The total number of articles published grew roughly 5-fold in the last 5 years, with the nonintegration focused articles mainly driving this growth. Additionally, a minority of type 2 articles addressed bias (22%) and explainability (16%). These articles were primarily led by radiologists (63%), with most (60%) having additional postgraduate degrees.

AI/ML publications have been rapidly increasing in neuroradiology with only a minority of this growth being attributable to end-user application. Areas identified for improvement include enhancing the quality of type 2 articles, namely external validation, and addressing both bias and explainability. These results ultimately provide authors, editors, clinicians, and policymakers important insights to promote a shift toward integrating practical AI/ML solutions in neuroradiology.

The rapidly increasing adoption of large language models in medicine has drawn attention to potential applications within the field of neurosurgery. This study evaluates the effects of various contextualization methods on ChatGPT's ability to provide expert-consensus aligned recommendations on the diagnosis and management of Chiari Malformation and Syringomyelia.

Native GPT4 and GPT4 models contextualized using various strategies were asked questions revised from the 2022 Chiari and Syringomyelia Consortium International Consensus Document. ChatGPT-provided responses were then compared to consensus statements using reviewer assessments of 1) responding to the prompt, 2) agreement of ChatGPT response with consensus statements, 3) recommendation to consult with a medical professional, and 4) presence of supplementary information. Flesch-Kincaid, SMOG, word count, and Gunning-Fog readability scores were calculated for each model using the quanteda package in R.

Relative to GPT4, all contextualized GPTs demonstrated increased agreement with consensus statements. PDF+Prompting and Prompting models provided the most elevated agreement scores of 19 of 24 and 23 of 24, respectively, versus 9 of 24 for GPT4 (p=.021, p=.001). A trend toward improved readability was observed when comparing contextualized models at large to ChatGPT4, with significant decreases in average word count (180.7 vs 382.3, p<.001) and Flesch-Kincaid Reading Ease score (11.7 vs 17.2, p=.033).

The enhanced performance observed in response to ChatGPT4 contextualization suggests broader applications of large language models in neurosurgery than what the current literature indicates. This study provides proof of concept for the use of contextualized GPT models in neurosurgical contexts and showcases the easy accessibility of improved model performance.

Significant strides will be made in the field of computerized electrocardiology through the development of artificial intelligence (AI)-enhanced ECG (AI-ECG) algorithms. Yet, the scientific discourse has primarily relied upon on retrospective analyses for deriving and externally validating AI-ECG classification algorithms, an approach that fails to fully judge their real-world effectiveness or reveal potential unintended consequences. Prospective trials and analyses of AI-ECG algorithms will be crucial for assessing real-world diagnostic scenarios and understanding their practical utility and degree influence they confer onto clinicians. However, conducting such studies is challenging due to their resource-intensive nature and associated technical and logistical hurdles. To overcome these challenges, we propose an innovative approach to assess AI-ECG algorithms using a virtual testing environment. This strategy can yield critical insights into the practical utility and clinical implications of novel AI-ECG algorithms. Moreover, such an approach can enable an assessment of the influence of AI-ECG algorithms have their users. Herein, we outline a proposed randomized control trial for evaluating the diagnostic efficacy of new AI-ECG algorithm(s) specifically designed to differentiate between wide complex tachycardias into ventricular tachycardia and supraventricular wide complex tachycardia.

While breast cancer treatments have advanced significantly nowadays, yet metastatic, especially triple-negative breast cancer (TNBC), remains challenging with low survival. Cancer immunotherapy, a promising approach for HER2-positive and TNBC, still faces resistance hurdles. Recently, numerous studies have set their sights on the resistance of immunotherapy for breast cancer. Our study provides a thorough comprehension of the current research landscape, hotspots, and emerging breakthroughs in this critical area through a meticulous bibliometric analysis. As of March 26, 2024, a total of 1341 articles on immunology resistance in breast cancer have been gathered from Web of Science Core Collection, including 765 articles and 576 reviews. Bibliometrix, CiteSpace and VOSviewer software were utilized to examine publications and citations per year, prolific countries, contributive institutions, high-level journals and scholars, as well as highly cited articles, references and keywords. The research of immunotherapy resistance in breast cancer has witnessed a remarkable surge over the past seven years. The United States and China have made significant contributions, with Harvard Medical School being the most prolific institution and actively engaging in collaborations. The most contributive author is Curigliano, G from the European Institute of Oncology in Italy, while Wucherpfennig, K. W. from the Dana-Farber Cancer Institute in the USA, had the highest citations. Journals highly productive primarily focus on clinical, immunology and oncology research. Common keywords include "resistance", "expression", "tumor microenvironment", "cancer", "T cell", "therapy", "chemotherapy" and "cell". Current research endeavors to unravel the mechanisms of immune resistance in breast cancer through the integration of bioinformatics, basic experiments, and clinical trials. Efforts are underway to develop strategies that improve the effectiveness of immunotherapy, including the exploration of combination therapies and advancements in drug delivery systems. Additionally, there is a strong focus on identifying novel biomarkers that can predict patient response to immunology. This study will provide researchers with an up-to-date overview of the present knowledge in drug resistance of immunology for breast cancer, serving as a valuable resource for informed decision-making and further research on innovative approaches to address immunotherapy resistance.

Industry 4.0 (I4.0) technologies have improved operations in health care facilities by optimizing processes, leading to efficient systems and tools to assist health care personnel and patients.

This study investigates the current implementation and impact of I4.0 technologies within maternal health care, explicitly focusing on transforming care processes, treatment methods, and automated pregnancy monitoring. Additionally, it conducts a thematic landscape mapping, offering a nuanced understanding of this emerging field. Building on this analysis, a future research agenda is proposed, highlighting critical areas for future investigations.

A bibliometric analysis of publications retrieved from the Scopus database was conducted to examine how the research into I4.0 technologies in maternal health care evolved from 1985 to 2022. A search strategy was used to screen the eligible publications using the abstract and full-text reading. The most productive and influential journals; authors', institutions', and countries' influence on maternal health care; and current trends and thematic evolution were computed using the Bibliometrix R package (R Core Team).

A total of 1003 unique papers in English were retrieved using the search string, and 136 papers were retained after the inclusion and exclusion criteria were implemented, covering 37 years from 1985 to 2022. The annual growth rate of publications was 9.53%, with 88.9% (n=121) of the publications observed in 2016-2022. In the thematic analysis, 4 clusters were identified-artificial neural networks, data mining, machine learning, and the Internet of Things. Artificial intelligence, deep learning, risk prediction, digital health, telemedicine, wearable devices, mobile health care, and cloud computing remained the dominant research themes in 2016-2022.

This bibliometric analysis reviews the state of the art in the evolution and structure of I4.0 technologies in maternal health care and how they may be used to optimize the operational processes. A conceptual framework with 4 performance factors-risk prediction, hospital care, health record management, and self-care-is suggested for process improvement. a research agenda is also proposed for governance, adoption, infrastructure, privacy, and security.

With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research.

The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally.

This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels.

To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers.

The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.

Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows.

The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics.

Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework.

Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.

The analysis aims to provide a comprehensive understanding of the current landscape of research on the Intestinal barrier damage after traumatic brain injury (TBI), elucidate specific mechanisms, and address knowledge gaps to help guide the development of targeted therapeutic interventions and improve outcomes for individuals with TBI. A total of 2756 relevant publications by 13,778 authors affiliated within 3198 institutions in 79 countries were retrieved from the Web of Science. These publications have been indexed by 1139 journals and cited 158, 525 references. The most productive author in this field was Sikiric P, and the University of Pittsburgh was identified as the most influential institution. The United States was found to be the leading country in terms of article output and held a dominant position in this field. The International Journal of Molecular Sciences was identified as a major source of publications in this area. In terms of collaboration, the cooperation between the United States and China was found to be the most extensive among countries, institutions, and authors, indicating a high level of influence in this field. Keyword co-occurrence network analysis revealed several hotspots in this field, including the microbiome-gut-brain axis, endoplasmic reticulum stress, cellular autophagy, ischemia-reperfusion, tight junctions, and intestinal permeability. The analysis of keyword citation bursts suggested that ecological imbalance and gut microbiota may be the forefront of future research. The findings of this study can serve as a reference and guiding perspective for future research in this field.

As Artificial Intelligence (AI) becomes pervasive in healthcare, including applications like robotic surgery and image analysis, the World Medical Association emphasises integrating AI education into medical curricula. This study evaluates medical students' perceptions of 'AI in medicine', their preferences for AI training in education, and their grasp of AI's ethical implications in healthcare.

A cross-sectional study was conducted among 325 medical students in Kerala using a pre-validated, semi structured questionnaire. The survey collected demographic data, any past educational experience about AI, participants' self-evaluation of their knowledge and evaluated self-perceived understanding of applications of AI in medicine. Participants responded to twelve Likert-scale questions targeting perceptions and ethical aspects and their opinions on suggested topics on AI to be included in their curriculum.

AI was viewed as an assistive technology for reducing medical errors by 57.2% students and 54.2% believed AI could enhance medical decision accuracy. About 49% agreed that AI could potentially improve accessibility to healthcare. Concerns about AI replacing physicians were reported by 37.6% and 69.2% feared a reduction in the humanistic aspect of medicine. Students were worried about challenges to trust (52.9%), patient-physician relationships (54.5%) and breach of professional confidentiality (53.5%). Only 3.7% felttotally competent in informing patients about features and risks associated with AI applications. Strong demand for structured AI training was expressed, particularly on reducing medical errors (76.9%) and ethical issues (79.4%).

This study highlights medical students' demand for structured AI training in undergraduate curricula, emphasising its importance in addressing evolving healthcare needs and ethical considerations. Despite widespread ethical concerns, the majority perceive AI as an assistive technology in healthcare. These findings provide valuable insights for curriculum development and defining learning outcomes in AI education for medical students.

The interrelation between COVID-19 and various cardiovascular and metabolic disorders has been a critical area of study. There is a growing need to understand how comorbidities such as cardiovascular diseases (CVDs) and metabolic disorders affect the risk and severity of COVID-19.

The objective of this study is to systematically analyze the association between COVID-19 and cardiovascular and metabolic disorders. The focus is on comorbidity, examining the roles of CVDs such as embolism, thrombosis, hypertension, and heart failure, as well as metabolic disorders such as disorders of glucose and iron metabolism.

Our study involved a systematic search in PubMed for literature published from 2000 to 2022. We established 2 databases: one for COVID-19-related articles and another for CVD-related articles, ensuring all were peer-reviewed. In terms of data analysis, statistical methods were applied to compare the frequency and relevance of MeSH (Medical Subject Headings) terms between the 2 databases. This involved analyzing the differences and ratios in the usage of these terms and employing statistical tests to determine their significance in relation to key CVDs within the COVID-19 research context.

The study revealed that "Cardiovascular Diseases" and "Nutritional and Metabolic Diseases" were highly relevant as level 1 Medical Subject Headings descriptors in COVID-19 comorbidity research. Detailed analysis at level 2 and level 3 showed "Vascular Disease" and "Heart Disease" as prominent descriptors under CVDs. Significantly, "Glucose Metabolism Disorders" were frequently associated with COVID-19 comorbidities such as embolism, thrombosis, and heart failure. Furthermore, iron deficiency (ID) was notably different in its occurrence between COVID-19 and CVD articles, underlining its significance in the context of COVID-19 comorbidities. Statistical analysis underscored these differences, highlighting the importance of both glucose and iron metabolism disorders in COVID-19 research.

This work lays the foundation for future research that utilizes a knowledge-based approach to elucidate the intricate relationships between these conditions, aiming to develop more effective health care strategies and interventions in the face of ongoing pandemic challenges.

China has entered the era of digital health care after years of reforms in the health care system. The use of digital technologies in healthcare services is rapidly increasing, indicating the onset of a new period. The reform of health insurance has also entered a new phase.

This study aims to investigate the evolution of health care insurance within the context of telemedicine and Internet Plus Healthcare (IPHC) during the digital health care era by using scientometric methods to analyze publication patterns, influential keywords, and research hot spots. It seeks to understand how health care insurance has adapted to the growing integration of IPHC and telemedicine in health care services and the implications for policy and practice.

A total of 411 high-quality studies were curated from the China National Knowledge Infrastructure (CNKI) database in the Chinese language, scientometric analysis was conducted, and VOSviewer software was used to conduct a visualized analysis of keywords and hot spots in the literature.

The number of articles in this field has increased notably from 2000 to 2022 and has increased annually based on a curve of y=0.332exp (0.4002x) with R2=0.6788. In total, 62 institutions and 811 authors have published research articles in the Chinese language in this field. This study included 290 keywords and formulated a total of 5 hot-topic clusters of "telemedicine," "IPHC," "internet hospital," "health insurance payments," and "health insurance system."

Studies on the application of digital technologies in health care insurance has evolved from foundational studies to a broader scope. The emergence of internet hospitals has showcased the potential for integrating IPHC services into insurance payment systems. However, this development also highlights the necessity for enhanced interregional coordination mechanisms. The reform of health insurance payment is contingent upon ongoing advancements in digital technology and increased investment in electronic medical records and primary health care services. Future efforts should focus on integrating technology with administrative systems, advancing mobile health care solutions, and ensuring interoperability among various payment systems to improve efficiency and standardize health care services.

Control charts, used in healthcare operations to monitor process stability and quality, are essential for ensuring patient safety and improving clinical outcomes. This comprehensive research study aims to provide a thorough understanding of the role of control charts in healthcare quality monitoring and future perspectives by utilizing a dual methodology approach involving a systematic review and a pioneering bibliometric analysis. A systematic review of 73 out of 223 articles was conducted, synthesizing existing literature (1995-2023) and revealing insights into key trends, methodological approaches, and emerging themes of control charts in healthcare. In parallel, a bibliometric analysis (1990-2023) on 184 articles gathered from Web of Science and Scopus was performed, quantitatively assessing the scholarly landscape encompassing control charts in healthcare. Among 25 countries, the USA is the foremost user of control charts, accounting for 33% of all applications, whereas among 14 health departments, epidemiology leads with 28% of applications. The practice of control charts in health monitoring has increased by more than one-third during the last 3 years. Globally, exponentially weighted moving average charts are the most popular, but interestingly the USA remained the top user of Shewhart charts. The study also uncovers a dynamic landscape in healthcare quality monitoring, with key contributors, research networks, research hotspot tendencies, and leading countries. Influential authors, such as J.C. Benneyan, W.H. Woodall, and M.A. Mohammed played a leading role in this field. In-countries networking, USA-UK leads the largest cluster, while other clusters include Denmark-Norway-Sweden, China-Singapore, and Canada-South Africa. From 1990 to 2023, healthcare monitoring evolved from studying efficiency to focusing on conditional monitoring and flowcharting, with human health, patient safety, and health surveys dominating 2011-2020, and recent years emphasizing epidemic control, COronaVIrus Disease of 2019 (COVID-19) statistical process control, hospitals, and human health monitoring using control charts. It identifies a transition from conventional to artificial intelligence approaches, with increasing contributions from machine learning and deep learning in the context of Industry 4.0. New researchers and journals are emerging, reshaping the academic context of control charts in healthcare. Our research reveals the evolving landscape of healthcare quality monitoring, surpassing traditional reviews. We uncover emerging trends, research gaps, and a transition in leadership from established contributors to newcomers amidst technological advancements. This study deepens the importance of control charts, offering insights for healthcare professionals, researchers, and policymakers to enhance healthcare quality. Future challenges and research directions are also provided.

In recent years, research on dysphagia has gained significant traction as one of the key topics of oral health research pertaining to the aged. Numerous academics have studied dysphagia in great detail and have produced numerous excellent scientific research findings.

To review the literature regarding dysphagia in community-dwelling older adults and identify the knowledge and trends using bibliometric methods.

The literature on dysphagia in older adults in the community was gathered from the Web of Science Core Collection (WoSCC), with inclusion criteria specifying English-language publications. The retrieval deadline was November 28, 2022. We extracted the following data: title, year, abstract, author, keywords, institution, and cited literature, and used CiteSpace (version 6.1.R3) to visualize the data through the knowledge map, burst keyword analysis, cluster analysis, and collaborative network analysis.

A total of 979 articles and reviews were retrieved. Regarding productivity, the top 2 countries were the United States (n =239) and Japan (n =236). Hidetaka Wakabayashi (n =26) was one of the most prolific writers. The first paper in the frequency ranking of references cited was a white paper: European Society for Swallowing Disorders and European Union Geriatric Medicine Society white paper: oropharyngeal dysphagia as a geriatric syndrome (n =53). "Prevalence" (n =173), "risk factor" (n =119), and "aspiration pneumonia" (n =108) were the most frequently occurring keywords (excluding defining nouns). The study identified reliability, tongue pressure, home discharge, and swallowing function as research hotspots from 2020 to 2022.

Prevalence, risk factors, and pneumonia are significant areas of study. Tongue pressure and sarcopenia are research hotspots and potential targets. In the future, research on dysphagia needs to refine strategies for prevention and control, as well as provide tertiary preventative services.

Aim: A bibliometric analysis and evaluation of research on non-Helicobacter pylori Helicobacter species (NHPHs) is essential to determining future research directions. Materials & methods: A comprehensive search was carried out using predetermined search terms within the Web of Science Core Collection (WoSCC) to gather publications spanning from 1993 to 2023. VOSviewer and Citespace were employed for data analysis and visualization. Results: 308 publications on NHPHs were included. Among these, gastric NHPHs received more publications and attention compared with enterohepatic NHPHs. Key findings included the identification of most productive countries, institutions, journals, authors, keywords, research trends and notable perspectives in the field. Conclusion: The article guides further research and clinical applications on NHPHs.

Information on the application of artificial intelligence (AI) in healthcare is needed to align healthcare transformation efforts. This bibliometric analysis aims to establish the patterns of publication activities on the application of AI in health. A total of 1083 scholarly papers published between 1993 and 2023 were retrieved from the Web of Science and Scopus databases. R Studio and VOSviewer were applied to quantify and illustrate publication patterns and citation rates. Publication rates grew by an average rate of 13% yearly, with each document being cited averagely 12 times. The articles had a mean of five co-authors, with a global co-authorship rate of 10%. COVID-19, artificial intelligence, and machine learning dominated the publications. The US, China, UK, Canada, and India coordinated most of the collaborative research. AI-based health information research is growing steadily. International collaborations can be leveraged to ensure the spread and interoperability of AI-based healthcare innovations globally.

To analyze the hotspots and trends in artificial intelligence (AI) research in the field of cataracts.

The Science Citation Index Expanded of the Web of Science Core Collection was used to collect the research literature related to AI in the field of cataracts, which was analyzed for valuable information such as years, countries/regions, journals, institutions, citations, and keywords. Visualized co-occurrence network graphs were generated through the library online analysis platform, VOSviewer, and CiteSpace tools.

A total of 222 relevant research articles from 41 countries were selected. Since 2019, the number of related articles has increased significantly every year. China (n = 82, 24.92%), the United States (n = 55, 16.72%) and India (n = 26, 7.90%) were the three countries with the most publications, accounting for 49.54% of the total. The Journal of Cataract and Refractive Surgery (n = 13, 5.86%) and Translational Vision Science & Technology (n = 10, 4.50%) had the most publications. Sun Yat-sen University (n = 25, 11.26%), the Chinese Academy of Sciences (n = 17, 7.66%), and Capital Medical University (n = 16, 7.21%) are the three institutions with the highest number of publications. We discovered through keyword analysis that cataract, diagnosis, imaging, classification, intraocular lens, and formula are the main topics of current study.

This study revealed the hot spots and potential trends of AI in terms of cataract diagnosis and intraocular lens power calculation. AI will become more prevalent in the field of ophthalmology in the future.

This study conducts a bibliometric analysis of literature on the treatment of inflammatory bowel disease (IBD) with traditional Chinese medicine (TCM) to explore its research status, hotspots, and development trends, providing ideas and references for further research.

We screened literature for treating IBD with TCM from the Web of Science Core Collection (WOSCC), and used the VOSviewer software (1.6.18) to discover cooperation among countries, institutions, authors, and information on journals, keywords, etc. We use the CiteSpace software (6.2.R2) to analyze co-citation and burst discovery of references.

In all, 440 relevant literature papers were searched and screened from the WOSCC database. The results showed that the number of publications concerning treating IBD with TCM has shown a significant growth in the past decade. China is far ahead in terms of article output, occupying a dominant position. The institution with the most published articles is Nanjing University of Traditional Chinese Medicine. The authors who have published most of the articles are Dai Yancheng, Shi Rui, and Zhou Lian. The Journal of Ethnopharmacology published maximum articles in this field, while Gastroenterology was the most cited journal. Ungaro et al.'s article entitled "Ulcerative colitis" (https://doi.org/10.1016/S0140-6736(16)32126-2), published in The Lancet in 2017 was the most cited study. The high-frequency keywords mainly include ulcerative colitis, inflammation, NF-κB, expression, traditional Chinese medicine, gut microbiota, activation, mice, cells, etc.

The research heat for treating IBD with TCM has risen over the past decade, with studies focusing on three main aspects: clinical studies of TCM, basic pharmacology, and animal experimental research. The research hotspot shifted from pathogenesis, clinical study of TCM, basic pharmacology, and complementary therapies to the study of network pharmacology and the mechanism of action of TCM related to gut microbiota. Network pharmacology and gut microbiota are at the frontiers of research and turning to be the future research trends to provide new insights and ideas for further research for treating IBD with TCM.

The integration of artificial intelligence (AI) into health care has seen remarkable advancements, with applications extending to animal health. This article explores the potential benefits and challenges associated with employing AI chatbots as tools for pet health care. Focusing on ChatGPT, a prominent language model, the authors elucidate its capabilities and its potential impact on pet owners' decision-making processes. AI chatbots offer pet owners access to extensive information on animal health, research studies and diagnostic options, providing a cost-effective and convenient alternative to traditional veterinary consultations. The fate of a case involving a Border Collie named Sassy demonstrates the potential benefits of AI in veterinary medicine. In this instance, ChatGPT played a pivotal role in suggesting a diagnosis that led to successful treatment, showcasing the potential of AI chatbots as valuable tools in complex cases. However, concerns arise regarding pet owners relying solely on AI chatbots for medical advice, potentially resulting in misdiagnosis, inappropriate treatment and delayed professional intervention. We emphasize the need for a balanced approach, positioning AI chatbots as supplementary tools rather than substitutes for licensed veterinarians. To mitigate risks, the article proposes strategies such as educating pet owners on AI chatbots' limitations, implementing regulations to guide AI chatbot companies and fostering collaboration between AI chatbots and veterinarians. The intricate web of responsibilities in this dynamic landscape underscores the importance of government regulations, the educational role of AI chatbots and the symbiotic relationship between AI technology and veterinary expertise. In conclusion, while AI chatbots hold immense promise in transforming pet health care, cautious and informed usage is crucial. By promoting awareness, establishing regulations and fostering collaboration, the article advocates for a responsible integration of AI chatbots to ensure optimal care for pets.

This paper analyzed the research on risk management in the doctor-patient relationship (DPR) based on a systematic quantitative literature review approach using bibliometric software. It aims to uncover potential information about current research and predict future research hotspots and trends.

We conducted a comprehensive search for relevant publications in the Scopus database and the Web of Science Core Collection database from January 1, 2000 to December 31, 2023. We analyzed the data using CiteSpace 6.2.R2 and VOSviewer 1.6.19 software to examine the annual number of publications, countries/regions, journals, citations, authors, and keywords in the field.

A total of 553 articles and reviews that met the criteria were included in this study. There is an overall upward trend in the number of publications issued; in terms of countries/regions, the United States and the United Kingdom are the largest contributors; Patient Education and Counseling is the most productive journal (17); Physician communication and patient adherence to treatment: a meta-analysis is the most cited article (1637); the field has not yet to form a stable and obvious core team; the analysis of high-frequency keywords revealed four main research directions: the causes of DPR risks, coping strategies, measurement tools, and research related to people prone to doctor-patient risk characteristics; the causes of DPR risks, coping strategies, measurement tools, and research related to people prone to doctor-patient risk characteristics; the keyword burst analysis revealed several shifts in the research hotspots for risk management in the DPR, suggesting that chronic disease management, is a future research direction for the continued development of risk management in the DPR.

The visualization analysis of risk management literature in the DPR using CiteSpace and VOSviewer software provides insights into the current research status and highlights future research directions.

Artificial intelligence (AI) has garnered considerable attention in the context of sepsis research, particularly in personalized diagnosis and treatment. Conducting a bibliometric analysis of existing publications can offer a broad overview of the field and identify current research trends and future research directions.

The objective of this study is to leverage bibliometric data to provide a comprehensive overview of the application of AI in sepsis.

We conducted a search in the Web of Science Core Collection database to identify relevant articles published in English until August 31, 2023. A predefined search strategy was used, evaluating titles, abstracts, and full texts as needed. We used the Bibliometrix and VOSviewer tools to visualize networks showcasing the co-occurrence of authors, research institutions, countries, citations, and keywords.

A total of 259 relevant articles published between 2014 and 2023 (until August) were identified. Over the past decade, the annual publication count has consistently risen. Leading journals in this domain include Critical Care Medicine (17/259, 6.6%), Frontiers in Medicine (17/259, 6.6%), and Scientific Reports (11/259, 4.2%). The United States (103/259, 39.8%), China (83/259, 32%), United Kingdom (14/259, 5.4%), and Taiwan (12/259, 4.6%) emerged as the most prolific countries in terms of publications. Notable institutions in this field include the University of California System, Emory University, and Harvard University. The key researchers working in this area include Ritankar Das, Chris Barton, and Rishikesan Kamaleswaran. Although the initial period witnessed a relatively low number of articles focused on AI applications for sepsis, there has been a significant surge in research within this area in recent years (2014-2023).

This comprehensive analysis provides valuable insights into AI-related research conducted in the field of sepsis, aiding health care policy makers and researchers in understanding the potential of AI and formulating effective research plans. Such analysis serves as a valuable resource for determining the advantages, sustainability, scope, and potential impact of AI models in sepsis.

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.