This study aimed to assess the readability, accuracy, appropriateness, and overall quality of responses generated by large language models (LLMs), including ChatGPT-3.5, Microsoft Copilot, and Gemini (Version 2.0 Flash), to frequently asked questions (FAQs) related to root canal retreatment.

Three LLM chatbots-ChatGPT-3.5, Microsoft Copilot, and Gemini (Version 2.0 Flash)-were assessed based on their responses to 10 patient FAQs. Readability was analyzed using seven indices, including Flesch reading ease score (FRES), Flesch-Kincaid grade level (FKGL), Simple Measure of Gobbledygook (SMOG), gunning FOG (GFOG), Linsear Write (LW), Coleman-Liau (CL), and automated readability index (ARI), and compared against the recommended sixth-grade reading level. Response quality was evaluated using the Global Quality Scale (GQS), while accuracy and appropriateness were rated on a five-point Likert scale by two independent reviewers. Statistical analyses were conducted using one-way ANOVA, Tukey or Games-Howell post-hoc tests for continuous variables. Spearman's correlation test was used to assess associations between categorical variables.

All chatbots generated responses exceeding the recommended readability level, making them suitable for readers at or above the 10th-grade level. No significant difference was found between ChatGPT-3.5 and Microsoft Copilot, while Gemini produced significantly more readable responses (p < 0.05). Gemini demonstrated the highest proportion of accurate (80 %) and high-quality responses (80 %) compared to ChatGPT-3.5 and Microsoft Copilot.

None of the chatbots met the recommended readability standards for patient education materials. While Gemini demonstrated better readability, accuracy, and quality, all three models require further optimization to enhance accessibility and reliability in patient communication.

Mitochondria, essential organelles responsible for cellular energy production, emerge as a key factor in the pathogenesis of neurodegenerative disorders. This review explores advancements in mitochondrial biology studies that highlight the pivotal connection between mitochondrial dysfunctions and neurological conditions such as Alzheimer's, Parkinson's, Huntington's, ischemic stroke, and vascular dementia. Mitochondrial DNA mutations, impaired dynamics, and disruptions in the ETC contribute to compromised energy production and heightened oxidative stress. These factors, in turn, lead to neuronal damage and cell death. Recent research has unveiled potential therapeutic strategies targeting mitochondrial dysfunction, including mitochondria targeted therapies and antioxidants. Furthermore, the identification of reliable biomarkers for assessing mitochondrial dysfunction opens new avenues for early diagnosis and monitoring of disease progression. By delving into these advancements, this review underscores the significance of understanding mitochondrial biology in unraveling the mechanisms underlying neurodegenerative disorders. It lays the groundwork for developing targeted treatments to combat these devastating neurological conditions.

Artificial Intelligence (AI) is capable of revolutionizing cancer therapy and advancing precision oncology via integrating genomics data and digitized health information. AI applications show promise in cancer prediction, prognosis, and treatment planning, particularly in radiomics, deep learning, and machine learning for early cancer diagnosis. However, widespread adoption requires comprehensive data and clinical validation. While AI has demonstrated advantages in treating common malignancies like lung and breast cancers, challenges remain in managing rare tumors due to limited datasets. AI's role in processing multi-omics data and supporting precision oncology decision-making is critical as genetic and health data become increasingly digitized.

This review article presents current knowledge on AI and associated technologies, which are being utilized in the diagnosis and therapy of cancer. The applications of AI in radiomics, deep learning, and machine learning for cancer screening and treatment planning are examined. The study also explores the capabilities and limitations of predictive AI in diagnosis and prognosis, as well as generative AI, such as advanced chatbots, in patient and provider interactions.

AI can improve the early diagnosis and treatment of high-incidence cancers like breast and lung cancer. However, its application in rare cancers is limited by insufficient data for training and validation. AI can effectively process large-scale multi-omics data from DNA and RNA sequencing, enhancing precision oncology. Predictive AI aids in risk assessment and prognosis, while generative AI tools improve patient-provider communication. Despite these advancements, further research and technological progress are needed to overcome existing challenges.

AI holds transformative potential for cancer therapy, particularly in precision oncology, early detection, and personalized treatment planning. However, challenges such as data limitations in rare cancers, the need for clinical validation, and regulatory considerations must be addressed. Future advancements in AI could significantly improve decision-support systems in oncology, ultimately enhancing patient care and quality of life. The review highlights both the opportunities and obstacles in integrating AI into cancer diagnostics and therapeutics, calling for continued research and regulatory oversight.

The use of Artificial Intelligence (AI) is exponentially rising in the healthcare sector. This change influences various domains of early identification, diagnosis, and treatment of diseases.

This study examines the integration of AI in healthcare, focusing on its transformative potential in diagnostics and treatment, and the challenges and methodologies. shaping its future development.

The review included 68 academic studies retracted from different databases (WOS, Scopus and Pubmed) from January 2020 and April 2024. After careful review and data analysis, AI methodologies, benefits and challenges, were summarized.

The number of studies showed a steady rise from 2020 to 2023. Most of them were the results of a collaborative work with international universities (92.1%). The majority (66.7%) were published in top-tier (Q1) journals and 40% were cited 2-10 times. The results have shown that AI tools such as deep learning methods and machine learning continue to significantly improve accuracy and timely execution of medical processes. Benefits were discussed from both the organizational and the patient perspective in the categories of diagnosis, treatment, consultation and health monitoring of diseases. However, some challenges may exist, despite these benefits, and are related to data integration, errors related to data processing and decision making, and patient safety.

The article examines the present status of AI in medical applications and explores its potential future applications. The findings of this review are useful for healthcare professionals to acquire deeper knowledge on the use of medical AI from design to implementation stage. However, a thorough assessment is essential to gather more insights into whether AI benefits outweigh its risks. Additionally, ethical and privacy issues need careful consideration.

Intimate Partner Violence (IPV) is a major public health problem to be addressed with innovative and interconnecting strategies for ensuring the psychophysical health of the surviving woman. According to the World Health Organization, 27% of women worldwide have experienced physical and sexual IPV in their lifetime. Most of the studies on gender-based violence focus on short-term effects, while long-term effects are often marginally included even though they represent the most serious and complex consequences. The molecular mechanisms underlying stress-related disorders in IPV victims are multiple and include dysregulation of the hypothalamic-pituitary-adrenal axis, inflammatory response, epigenetic modifications, neurotransmitter imbalances, structural changes in the brain, and oxidative stress. This review aims to explore the long-term health consequences of intimate partner violence (IPV), emphasizing the biological and psychological mechanisms underlying stress-related disorders and resilience. By integrating findings from epigenetics, microbiome research, and artificial intelligence (AI)-based data analysis, we highlight novel strategies for mitigating IPV-related trauma and improving recovery pathways. Genome-wide environment interaction studies, enhanced by AI-assisted data analysis, offer a promising public health approach for identifying factors that contribute to stress-related disorders and those that promote resilience, thus guiding more effective prevention and intervention strategies.

Natural language processing (NLP) is a subfield of artificial intelligence that enables computers to process human language. As most human interactions today involve the internet and electronic devices, NLP tools quickly become indispensable to modern life. The use of NLP tools in medical practice and research is growing fast. This scoping review evaluates the current and potential future applications of NLP in kidney transplantation medicine.

We conducted an electronic literature search on NLP in the setting of kidney transplantation on PubMed, EMBASE, and Scopus from inception to August 26, 2024. Two independent investigators conducted the search strategy and reviewed abstracts and full texts; conflicts were resolved after discussion with a third and fourth author. A total of ten studies were included in the study.

The most commonly studied clinical applications of NLP in kidney transplantation are its use as an adjunct tool to facilitate early diagnosis of renal disease and as an effective predictor of graft loss and complications among kidney transplant recipients. Some researchers were able to predict organs at risk of delayed implant or discard by analyzing donors' EHR; this has the potential to improve organ utilization significantly. In clinical research, NLP tools can be tailored to perform specific tasks of interest on unstructured text. By studying n comments from social media and news websites, 1 group was able to gauge public perception of transplant policies and identify potential actions to improve access to transplant care.

NLP tools have only recently been introduced into medical research, but they are already significantly impacting kidney transplantation medicine. The literature demonstrates the potential to improve early diagnosis of renal failure, predict renal transplantation outcomes, improve organ utilization, and support advocacy and policymaking. With more widespread use of EHR globally and the continued development of NLP technology, these tools are poised to revolutionize the practice of renal transplantation.

Given the enormous output and pace of development of artificial intelligence (AI) methods in medical imaging, it can be challenging to identify the true success stories to determine the state-of-the-art of the field. This report seeks to provide the magnetic resonance imaging (MRI) community with an initial guide into the major areas in which the methods of AI are contributing to MRI in oncology. After a general introduction to artificial intelligence, we proceed to discuss the successes and current limitations of AI in MRI when used for image acquisition, reconstruction, registration, and segmentation, as well as its utility for assisting in diagnostic and prognostic settings. Within each section, we attempt to present a balanced summary by first presenting common techniques, state of readiness, current clinical needs, and barriers to practical deployment in the clinical setting. We conclude by presenting areas in which new advances must be realized to address questions regarding generalizability, quality assurance and control, and uncertainty quantification when applying MRI to cancer to maintain patient safety and practical utility.

This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI).

A literature search was conducted using the databases Embase, Medline, Scopus, and Web of Science, and captured articles were assessed for inclusion in the review. Data extraction was performed for the summary of the AI model used, and key findings of each article, advantages and disadvantages were identified.

Following full-text screening, 21 articles were included in the review. The review covers models for segmentation models applied to the whole brain, cerebral cortex, subcortical structures, the cerebellum, blood vessels, perivascular spaces, and the ventricles. Accuracy of segmentation was generally high, particularly for segmenting neuroanatomical structures in healthy cohorts.

The use of AI for automatic brain segmentation is generally highly accurate and fast for all regions of the human brain. Challenges include robustness to anatomical variability and pathology, largely due to difficulties with accessing sufficient training data.

The detectability index, originally developed in psychophysics, has been applied in medical imaging to integrate objective metrics with subjective assessments. This index accounts for both image processing properties and the limitations of the human visual system, thus enhancing the clinical efficacy of imaging technologies. By providing a single metric that captures multiple aspects of image quality, the detectability index offers a comprehensive evaluation of clinical images. Numerous applications of this index across various areas of medical imaging are documented in the literature, along with recommendations for its use in periodic performance evaluations of imaging devices. However, since different modalities of images may require different detectability indices, it is crucial to assess the adequacy of the properties of the image being analyzed and those from the adopted index. A thorough understanding of this metric, including its statistical nature and complex relationship with model observers, is essential to ensure its proper application and interpretation, and to prevent misuse. Medical physicists face the challenge of a lack of organized guidance on the detectability index, necessitating a comprehensive review of its merits and drawbacks. This paper aims to trace the origins, concepts, and clinical applications of the detectability index, offering insight into its strengths, limitations, and future potential. To achieve this, an extensive literature review was conducted, covering the evolution of the index from its early use in radar interpretation to its current applications in modern imaging techniques and future trends. The paper includes supplementary materials such as a compendium of fundamental concepts, ancillary information, and mathematical deductions to help readers less experienced in the subject.

Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a misalignment between AI systems and their users. To address the AI implementation gap, we propose a human-centric methodology, novel in RT, for an interface design of an AI-driven RT treatment error detection system.

A 5-day design sprint was set up with a multi-disciplinary team of clinical and research staff and a commercial company. In the design sprint, an interface was prototyped to aid medical physicists in catching treatment errors during daily treatment fractions using dose-guided RT (DGRT) with a portal imager.

The design sprint resulted in a simulated prototype of an interface supported by all stakeholders. Important features of an interface include the AI certainty metric, explainable AI features, feedback options, and decision aid. The prototype was well-received by expert users.

Using a co-creation strategy, which is a novel approach in RT, we were able to prototype a novel human-interpretable interface to detect RT treatment errors and aid the DGRT workflow. Users showed confidence that the overall design method and the proposed prototype could lead to a viable clinical implementation.

It is essential to evaluate performance and assess quality before applying artificial intelligence (AI) and machine learning (ML) models to clinical practice. This study utilized ML to predict patient wait times in the Emergency Department (ED), determine model performance accuracies, and conduct fairness evaluations to further assess ethnic disparities in using ML for wait time prediction among different patient populations in the ED. This retrospective observational study included adult patients (age ≥18 years) in the ED (n=173,856 visits) who were assigned an Emergency Severity Index (ESI) level of 3 at triage. Prolonged wait time was defined as waiting time ≥30 minutes. We employed extreme gradient boosting (XGBoost) for predicting prolonged wait times. Model performance was assessed with accuracy, recall, precision, F1 score, and false negative rate (FNR). To perform the global and local interpretation of feature importance, we utilized Shapley additive explanations (SHAP) to interpret the output from the XGBoost model. Fairness in ML models were evaluated across sensitive attributes (sex, race and ethnicity, and insurance status) at both subgroup and individual levels. We found that nearly half (48.43%, 84,195) of ED patient visits demonstrated prolonged ED wait times. XGBoost model exhibited moderate accuracy performance (AUROC=0.81). When fairness was evaluated with FNRs, unfairness existed across different sensitive attributes (male vs. female, Hispanic vs. Non-Hispanic White, and patients with insurances vs. without insurance). The predicted FNRs were lower among females, Hispanics, and patients without insurance compared to their counterparts. Therefore, XGBoost model demonstrated acceptable performance in predicting prolonged wait times in ED visits. However, disparities arise in predicting patients with different sex, race and ethnicity, and insurance status. To enhance the utility of ML model predictions in clinical practice, conducting performance assessments and fairness evaluations are crucial.

The landscape of healthcare is rapidly changing with the increasing usage of machine and deep learning artificial intelligence and digital tools to assist in various sectors. This study aims to analyze the feasibility of the implementation of artificial intelligence (AI) models into healthcare systems. This review included English-language publications from databases such as SCOPUS, PubMed, and Google Scholar between 2000 and 2024. AI integration in healthcare systems will assist in large-scale dataset analysis, access to healthcare information, surgery data and simulation, and clinical decision-making in addition to many other healthcare services. However, with the reliance on AI, issues regarding medical liability, cybersecurity, and health disparities can form. This necessitates updates and transparency on health policy, AI training, and cybersecurity measures. To support the implementation of AI in healthcare, transparency regarding AI algorithm training and analytical approaches is key to allowing physicians to trust and make informed decisions about the applicability of AI results. Transparency will also allow healthcare systems to adapt appropriately, provide AI services, and create viable security measures. Furthermore, the increased diversity of data used in AI algorithm training will allow for greater generalizability of AI solutions in patient care. With the growth of AI usage and interaction with patient data, security measures and safeguards, such as system monitoring and cybersecurity training, should take precedence. Stricter digital policy and data protection guidelines will add additional layers of security for patient data. This collaboration will further bolster security measures amongst different regions and healthcare systems in addition to providing more means to innovative care. With the growing digitization of healthcare, advancing cybersecurity will allow effective and safe implementation of AI and other digital systems into healthcare and can improve the safety of patients and their personal health information.

Although large language models like ChatGPT-4 have demonstrated competency in English, their performance for minority groups speaking underrepresented languages, as well as their ability to adapt to specific sociocultural nuances and regional cuisines, such as those in Central Asia (for example, Kazakhstan), still requires further investigation.

To evaluate and compare the effectiveness of the ChatGPT-4 system in providing personalized, evidence-based nutritional recommendations in English, Kazakh, and Russian in Central Asia.

This study was conducted from 15 May to 31 August, 2023. On the basis of 50 mock patient profiles, ChatGPT-4 generated dietary advice, and responses were evaluated for personalization, consistency, and practicality using a 5-point Likert scale. To identify significant differences between the 3 languages, the Kruskal-Wallis test was conducted. Additional pairwise comparisons for each language were carried out using the post hoc Dunn's test.

ChatGPT-4 showed a moderate level of performance in each category for English and Russian languages, whereas in Kazakh language, outputs were unsuitable for evaluation. The scores for English, Russian, and Kazakh were as follows: for personalization, 3.32 ± 0.46, 3.18 ± 0.38, and 1.01 ± 0.06; for consistency, 3.48 ± 0.43, 3.38 ± 0.39, and 1.09 ± 0.18; and for practicality, 3.25 ± 0.41, 3.37 ± 0.38, and 1.07 ± 0.15, respectively. The Kruskal-Wallis test indicated statistically significant differences in ChatGPT-4's performance across the 3 languages (P < 0.001). Subsequent post hoc analysis using Dunn's test showed that the performance in both English and Russian was significantly different from that in Kazakh.

Our findings show that, despite using identical prompts across 3 distinct languages, the ChatGPT-4's capability to produce sensible outputs is limited by the lack of training data in non-English languages. Thus, a customized large language model should be developed to perform better in underrepresented languages and to take into account specific local diets and practices.

Artificial intelligence is very likely to be a pioneering technology in arthroplasty, with a wide range of pre-, intra- and post-operative applications. The opportunities for patients, doctors and healthcare policy are considerable, especially in the context of optimized and individualized patient care.

Despite these diverse possibilities, there are currently only a few AI applications in routine clinical practice, mainly due to the limited availability of analyzable health data. AI systems are only as good as the data they are trained with. If the data is insufficient, incomplete or biased, the AI may draw false conclusions. The current results of such AI applications in arthroplasty must, therefore, be viewed critically, especially as previous data bases were not designed a priori for AI applications.

The successful integration of AI, therefore, requires a targeted focus on the development of a specific data structure. In order to exploit the full potential of AI, comprehensive clinical data volumes are required, which can only be realized through a multicentric approach. In this context, ethical and data protection issues remain a further question, and not only in orthopaedics. Cooperative efforts at national and international levels are, therefore, essential in order to research and develop new AI applications.

The deployment of Artificial Intelligence (AI) in healthcare has the potential to transform patient care through improved diagnostics, personalized treatment plans, and more efficient resource management. However, the effectiveness and fairness of AI are critically dependent on the data it learns from. Biased datasets can lead to AI outputs that perpetuate disparities, particularly affecting social minorities and marginalized groups.

This paper introduces the "Data Artifacts Glossary", a dynamic, open-source framework designed to systematically document and update potential biases in healthcare datasets. The aim is to provide a comprehensive tool that enhances the transparency and accuracy of AI applications in healthcare and contributes to understanding and addressing health inequities.

Utilizing a methodology inspired by the Delphi method, a diverse team of experts conducted iterative rounds of discussions and literature reviews. The team synthesized insights to develop a comprehensive list of bias categories and designed the glossary's structure. The Data Artifacts Glossary was piloted using the MIMIC-IV dataset to validate its utility and structure.

The Data Artifacts Glossary adopts a collaborative approach modeled on successful open-source projects like Linux and Python. Hosted on GitHub, it utilizes robust version control and collaborative features, allowing stakeholders from diverse backgrounds to contribute. Through a rigorous peer review process managed by community members, the glossary ensures the continual refinement and accuracy of its contents. The implementation of the Data Artifacts Glossary with the MIMIC-IV dataset illustrates its utility. It categorizes biases, and facilitates their identification and understanding.

The Data Artifacts Glossary serves as a vital resource for enhancing the integrity of AI applications in healthcare by providing a mechanism to recognize and mitigate dataset biases before they impact AI outputs. It not only aids in avoiding bias in model development but also contributes to understanding and addressing the root causes of health disparities.

To systematically map the knowledge landscape and development trends in artificial intelligence (AI) applications for antimicrobial resistance (AMR) research through bibliometric analysis, providing evidence-based insights to guide future research directions and inform strategic decision-making in this dynamic field.

A comprehensive bibliometric analysis was performed using the Web of Science Core Collection database for publications from 2014 to 2024. The analysis integrated multiple bibliometric approaches: VOSviewer for visualization of collaboration networks and research clusters, CiteSpace for temporal evolution analysis, and quantitative analysis of publication metrics. Key bibliometric indicators including co-authorship patterns, keyword co-occurrence, and citation impact were analyzed to delineate research evolution and collaboration patterns in this domain.

A collection of 2,408 publications was analyzed, demonstrating significant annual growth with publications increasing from 4 in 2014 to 549 in 2023 (22.7% of total output). The United States (707), China (581), and India (233) were the leading contributors in international collaborations. The Chinese Academy of Sciences (53), Harvard Medical School (43), and University of California San Diego (26) were identified as top contributing institutions. Citation analysis highlighted two major breakthroughs: AlphaFold's protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). Keyword analysis identified six enduring research clusters from 2014 to 2024: sepsis, artificial neural networks, antimicrobial resistance, antimicrobial peptides, drug repurposing, and molecular docking, demonstrating the sustained integration of AI in antimicrobial therapy development. Recent trends show increasing application of AI technologies in traditional approaches, particularly in MALDI-TOF MS for pathogen identification and graph neural networks for large-scale molecular screening.

This bibliometric analysis shows the importance of artificial intelligence in enhancing the progress in the discovery of antimicrobial drugs especially toward the fight against AMR. From enhancing the fast, efficient and predictive performance of drug discovery methods, current AI capabilities have revealed observable potential to be proactive in combating the ever-growing challenge of AMR worldwide. This study serves not only an identification of current trends, but also, and especially, offers a strategic approach to further investigations.

Artificial intelligence (AI) is the new medical hot topic, being applied mainly in specialties with a strong imaging component. In the domain of gynecology, AI has been tested and shown vast potential in several areas with promising results, with an emphasis on oncology. However, fewer studies have been made focusing on urogynecology, a branch of gynecology known for using multiple imaging exams (IEs) and tests in the management of women's pelvic floor health. This review aims to illustrate the current state of AI in urogynecology, namely with the use of machine learning (ML) and deep learning (DL) in diagnostics and as imaging tools, discuss possible future prospects for AI in this field, and go over its limitations that challenge its safe implementation.

Background: With the development of artificial intelligence (A.I.), the optical coherence tomography angiography (OCTA) analysis of progression in hypertensive retinopathy could be improved. Our purpose was to use the OCTA to study the effect of uncontrolled dyslipidemia and hypertensive retinopathy on the retinal microvasculature and to identify a potential software update of the A.I. secondary to the OCTA analysis. By using our most relevant data, the A.I. software can be upgraded by introducing new mathematic formulas between the OCTA parameters and the lipid level. Methods: We performed a prospective cohort study on 154 eyes of participants from Eastern Europe. We used a standardized protocol to collect data on past medical history of dyslipidemia and hypertension and OCTA to measure retinal vascular parameters. Results: The average age of the participants was 56.9 ± 9.1, with a minimum of 34 and a maximum of 82 and with a higher percentage of males: 55.8%. Statistically significant correlations were found for total cholesterol and skeleton total (r = -0.249; p = 0.029), foveal avascular zone (FAZ), circularity and low-density lipoprotein (LDL) (r = 0.313; p = 0.006), non-flow area (NFA) and LDL (r = 0.233; p = 0.042), and vascular flow area (VFA) and LDL (r = -0.354; p = 0.002). Conclusions: Subjects with dyslipidemia and progressive hypertensive retinopathy had a reduction in microvascular density and vascular flow, a focal capillary non-perfusion, and an increased FAZ. Thus, by improving the A.I. system, our research aims to provide better OCTA monitoring, which could help in the early-stage detection of progression and development of A.I. screening programs, leading to increased efficiency in diagnosing patients.

Cardiovascular diseases are some of the underlying reasons contributing to the relentless rise in mortality rates across the globe. In this regard, there is a genuine need to integrate advanced technologies into the medical realm to detect such diseases accurately. Moreover, numerous academic studies have been published using AI-based methodologies because of their enhanced accuracy in detecting heart conditions. This research extensively delineates the different heart conditions, e.g., coronary artery disease, arrhythmia, atherosclerosis, mitral valve prolapse/mitral regurgitation, and myocardial infarction, and their underlying reasons and symptoms and subsequently introduces AI-based detection methodologies for precisely classifying such diseases. The review shows that the incorporation of artificial intelligence in detecting heart diseases exhibits enhanced accuracies along with a plethora of other benefits, like improved diagnostic accuracy, early detection and prevention, reduction in diagnostic errors, faster diagnosis, personalized treatment schedules, optimized monitoring and predictive analysis, improved efficiency, and scalability. Furthermore, the review also indicates the conspicuous disparities between the results generated by previous algorithms and the latest ones, paving the way for medical researchers to ascertain the accuracy of these results through comparative analysis with the practical conditions of patients. In conclusion, AI in heart disease detection holds paramount significance and transformative potential to greatly enhance patient outcomes, mitigate healthcare expenditure, and amplify the speed of diagnosis.

Breastfeeding rates remain below the globally recommended levels, a situation associated with higher infant and neonatal mortality rates. The implementation of artificial intelligence (AI) could help improve and increase breastfeeding rates. This study aimed to identify and synthesize the current information on the use of AI in the analysis of human milk and breastfeeding.

A scoping review was conducted according to the PRISMA Extension for Scoping Reviews guidelines. The literature search, performed in December 2023, used predetermined keywords from the PubMed, Scopus, LILACS, and WoS databases. Observational and qualitative studies evaluating AI in the analysis of breastfeeding patterns and human milk composition have been conducted. A thematic analysis was employed to categorize and synthesize the data.

Nineteen studies were included. The primary AI approaches were machine learning, neural networks, and chatbot development. The thematic analysis revealed five major categories: 1. Prediction of exclusive breastfeeding patterns: AI models, such as decision trees and machine learning algorithms, identify factors influencing breastfeeding practices, including maternal experience, hospital policies, and social determinants, highlighting actionable predictors for intervention. 2. Analysis of macronutrients in human milk: AI predicted fat, protein, and nutrient content with high accuracy, improving the operational efficiency of milk banks and nutritional assessments. 3. Education and support for breastfeeding mothers: AI-driven chatbots address breastfeeding concerns, debunked myths, and connect mothers to milk donation programs, demonstrating high engagement and satisfaction rates. 4. Detection and transmission of drugs in breast milk: AI techniques, including neural networks and predictive models, identified drug transfer rates and assessed pharmacological risks during lactation. 5. Identification of environmental contaminants in milk: AI models predict exposure to contaminants, such as polychlorinated biphenyls, based on maternal and environmental factors, aiding in risk assessment.

AI-based models have shown the potential to increase breastfeeding rates by identifying high-risk populations and providing tailored support. Additionally, AI has enabled a more precise analysis of human milk composition, drug transfer, and contaminant detection, offering significant insights into lactation science and maternal-infant health. These findings suggest that AI can promote breastfeeding, improve milk safety, and enhance infant nutrition.

Recent advancements in artificial intelligence (AI) have shown significant potential in the medical field, although many applications are still in the research phase. This paper provides a comprehensive review of advancements in augmented reality (AR), mixed reality (MR), and virtual reality (VR) for surgical applications from 2019 to 2024 to accelerate the transition of AI from the research to the clinical phase. This paper also provides an overview of proposed databases for further use in extended reality (XR), which includes AR, MR, and VR, as well as a summary of typical research applications involving XR in surgical practices. Additionally, this paper concludes by discussing challenges and proposed solutions for the application of XR in the medical field. Although the areas of focus and specific implementations vary among AR, MR, and VR, current trends in XR focus mainly on reducing workload and minimizing surgical errors through navigation, training, and machine learning-based visualization. Through analyzing these trends, AR and MR have greater advantages for intraoperative surgical functions, whereas VR is limited to preoperative training and surgical preparation. VR faces additional limitations, and its use has been reduced in research since the first applications of XR, which likely suggests the same will happen with further development. Nonetheless, with increased access to technology and the ability to overcome the black box problem, XR's applications in medical fields and surgery will increase to guarantee further accuracy and precision while reducing risk and workload.

Background Artificial Intelligence (AI) is revolutionizing medical science, with significant implications for radiology. Understanding the knowledge, attitudes, perspectives, and practices of medical professionals and residents related to AI's role in radiology is crucial for effective integration. Methods A cross-sectional survey was conducted among members of the Indian Radiology & Imaging Association (IRIA), targeting practicing radiologists and residents across academic and non-academic institutions. An anonymous, self-administered online questionnaire assessed AI awareness, usage, and perceptions, distributed via medical networks and social media. Descriptive statistics and chi-square tests were used to analyze the data, with statistical analysis performed using R version 4.2.2 (R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/). Results The survey gathered responses from 404 participants nationwide. A significant portion (95.3%) demonstrated a keen interest in expanding their knowledge of AI and recommended implementing educational initiatives that increase exposure to AI. Considerable concern about losing their jobs to AI was observed only in 27.9% of respondents. More than two-thirds (86.6%) of the respondents opined that the AI curriculum should be taught during residency and 75.7% are interested in collaborating with software developers to learn and start AI at their workplace. Conclusion The survey highlights the growing importance of AI in radiology, underscoring the need for enhanced AI education and training in medical curricula.

Incorporating artificial intelligence (AI) into in vitro fertilization (IVF) laboratories signifies a significant advancement in reproductive medicine. AI technologies, such as neural networks, deep learning, and machine learning, promise to enhance quality control (QC) and quality assurance (QA) through increased accuracy, consistency, and operational efficiency. This comprehensive review examines the effects of AI on IVF laboratories, focusing on its role in automating processes such as embryo and sperm selection, optimizing clinical outcomes, and reducing human error. AI's data analysis and pattern recognition capabilities offer valuable predictive insights, enhancing personalized treatment plans and increasing success rates in fertility treatments. However, integrating AI also brings ethical, regulatory, and societal challenges, including concerns about data security, algorithmic bias, and the human-machine interface in clinical decision-making. Through an in-depth examination of current case studies, advancements, and future directions, this manuscript highlights how AI can revolutionize IVF by standardizing processes, improving patient outcomes, and advancing the precision of reproductive medicine. It underscores the necessity of ongoing research and ethical oversight to ensure fair and transparent applications in this sensitive field, assuring the responsible use of AI in reproductive medicine.

Artificial intelligence (AI) has become increasingly important in health care, generating both curiosity and concern. With a doctor-patient ratio of 1:834 in India, AI has the potential to alleviate a significant health care burden. Public perception plays a crucial role in shaping attitudes that can facilitate the adoption of new technologies. Similarly, the acceptance of AI-driven mental health interventions is crucial in determining their effectiveness and widespread adoption. Therefore, it is essential to study public perceptions and usage of existing AI-driven mental health interventions by exploring user experiences and opinions on their future applicability, particularly in comparison to traditional, human-based interventions.

This study aims to explore the use, perception, and acceptance of AI-driven mental health interventions in comparison to traditional, human-based interventions.

A total of 466 adult participants from India voluntarily completed a 30-item web-based survey on the use and perception of AI-based mental health interventions between November and December 2023.

Of the 466 respondents, only 163 (35%) had ever consulted a mental health professional. Additionally, 305 (65.5%) reported very low knowledge of AI-driven interventions. In terms of trust, 247 (53%) expressed a moderate level of Trust in AI-Driven Mental Health Interventions, while only 24 (5.2%) reported a high level of trust. By contrast, 114 (24.5%) reported high trust and 309 (66.3%) reported moderate Trust in Human-Based Mental Health Interventions; 242 (51.9%) participants reported a high level of stigma associated with using human-based interventions, compared with only 50 (10.7%) who expressed concerns about stigma related to AI-driven interventions. Additionally, 162 (34.8%) expressed a positive outlook toward the future use and social acceptance of AI-based interventions. The majority of respondents indicated that AI could be a useful option for providing general mental health tips and conducting initial assessments. The key benefits of AI highlighted by participants were accessibility, cost-effectiveness, 24/7 availability, and reduced stigma. Major concerns included data privacy, security, the lack of human touch, and the potential for misdiagnosis.

There is a general lack of awareness about AI-driven mental health interventions. However, AI shows potential as a viable option for prevention, primary assessment, and ongoing mental health maintenance. Currently, people tend to trust traditional mental health practices more. Stigma remains a significant barrier to accessing traditional mental health services. Currently, the human touch remains an indispensable aspect of human-based mental health care, one that AI cannot replace. However, integrating AI with human mental health professionals is seen as a compelling model. AI is positively perceived in terms of accessibility, availability, and destigmatization. Knowledge and perceived trustworthiness are key factors influencing the acceptance and effectiveness of AI-driven mental health interventions.

In the realm of dental tissue regeneration research, various constraints exist such as the potential variance in cell quality, potency arising from differences in donor tissue and tissue microenvironment, the difficulties associated with sustaining long-term and large-scale cell expansion while preserving stemness and therapeutic attributes, as well as the need for extensive investigation into the enduring safety and effectiveness in clinical settings. The adoption of artificial intelligence (AI) technologies has been suggested as a means to tackle these challenges. This is because, tissue regeneration research could be advanced through the use of diagnostic systems that incorporate mining methods such as neural networks (NN), fuzzy, predictive modeling, genetic algorithms, machine learning (ML), cluster analysis, and decision trees. This article seeks to offer foundational insights into a subset of AI referred to as artificial neural networks (ANNs) and assess their potential applications as essential decision-making support tools in the field of dentistry, with a particular focus on tissue engineering research. Although ANNs may initially appear complex and resource intensive, they have proven to be effective in laboratory and therapeutic settings. This expert system can be trained using clinical data alone, enabling their deployment in situations where rule-based decision-making is impractical. As ANNs progress further, it is likely to play a significant role in revolutionizing dental tissue regeneration research, providing promising results in streamlining dental procedures and improving patient outcomes in the clinical setting.

(1) Background: In recent years, large language models (LLMs) such as ChatGPT have gained significant attention in various fields, including dentistry. This scoping review aims to examine the current applications and explore potential uses of LLMs in the orthodontic domain, shedding light on how they might improve dental healthcare. (2) Methods: We carried out a comprehensive search in five electronic databases, namely PubMed, Scopus, Embase, ProQuest and Web of Science. Two authors independently screened articles and performed data extraction according to the eligibility criteria, following the PRISMA-ScR guideline. The main findings from the included articles were synthesized and analyzed in a narrative way. (3) Results: A total of 706 articles were searched, and 12 papers were eventually included. The applications of LLMs include improving diagnostic and treatment efficiency in orthodontics as well as enhancing communication with patients. (4) Conclusions: There is emerging research in countries worldwide on the use of LLMs in orthodontics, suggesting an upward trend in their acceptance within this field. However, the potential application of LLMs remains in its early stage, with a noticeable lack of extensive studies and tailored products to address specific clinical needs.

Background  The generation of innovative research ideas is crucial to advancing the field of medicine. As physicians face increasingly demanding clinical schedules, it is important to identify tools that may expedite the research process. Artificial intelligence may offer a promising solution by enabling the efficient generation of novel research ideas. This study aimed to assess the feasibility of using artificial intelligence to build upon existing knowledge by generating innovative research questions.  Methods  A comparative evaluation study was conducted to assess the ability of AI models to generate novel research questions. The prompt "research ideas for adolescent idiopathic scoliosis" was input into ChatGPT 3.5, Gemini 1.5, Copilot, and Llama 3. This resulted in an output of several research questions ranging from 10 questions to 14 questions. A keyword-friendly modified version of the AI-generated responses was searched in the PubMed database. Results were limited to manuscripts published in the English language from the year 2000 to the present. Each response was then cross-referenced to the PubMed search results and assigned an originality score of 0-5, with 0 being the most original and 5 being not original at all, by adding one numerical value for each paper already published on the topic. The mean originality scores were calculated manually by summing the originality scores from all the responses from each AI model and then dividing that sum by the respective number of prompts generated by the AI. The standard deviation of the originality scores for each AI was calculated using the standard deviation function (STDEV) function in Google Sheets (Google, Mountain View, California). Each AI was also evaluated on its percent novelty, the percentage of total generated responses that yielded an originality score of 0 when searched in PubMed.  Results  Each AI produced varying numbers of research prompts that were inputted into PubMed. The mean originality scores for ChatGPT, Gemini, Copilot, and Llama were 4.2 ± 1.9, 4.1 ± 1.3, 4.0 ± 1.6, and 3.8 ± 1.7, respectively. Of ChatGPT's 12 prompts, 16.67% were completely novel (no prior research had been conducted on the topic provided by the AI model). 10.00% of Copilot's 10 prompts were completely novel, and 8.33% of Llama's 12 prompts were completely novel. None of Gemini's fourteen responses yielded an originality score of 0.  Conclusions  Our findings demonstrate that ChatGPT, Llama, and Copilot are capable of generating novel ideas in orthopaedics research. As these models continue to evolve and become even more refined with time, physicians and scientists should consider incorporating them when brainstorming and planning their research studies.

Artificial intelligence has come to be the highlight in almost all fields of science. It uses various models and algorithms to detect patterns and specific findings to diagnose a disease with utmost accuracy. With the increasing need for accurate and precise diagnosis of disease, employing artificial intelligence models and concepts in healthcare setup can be beneficial.

The search engines and databases employed in this study are PubMed, ScienceDirect and Medline. Studies published between 1st January 2013 to 1st February 2023 were included in this analysis. The selected articles were screened preliminarily using the Rayyan web tool, after which investigators screened the selected articles individually. The risk of bias for the selected studies was assessed using QUADAS-2 tool specially designed to test bias among studies related to diagnostic test reviews.

In this review, 17 studies were included from a total of 12,173 studies. These studies were analysed for their sensitivity, accuracy, positive predictive value, specificity and negative predictive value in diagnosing barrette's neoplasia, cardiac arrest, esophageal adenocarcinoma, sepsis and gastrointestinal stromal tumors. All the studies reported heterogeneity with p-value <0.05 at confidence interval 95%.

The existing evidential data suggests that artificial intelligence can be highly helpful in the field of diagnosis providing maximum precision and early detection. This helps to prevent disease progression and also helps to provide treatment at the earliest. Employing artificial intelligence in diagnosis will define the advancement of health care environment and also be beneficial in every aspect concerned with treatment to illnesses.

Artificial intelligence (AI) tools utilizing machine learning (ML) have gained increasing utility in medicine and academia as a means of enhancing efficiency. ASReview is one such AI program designed to streamline the systematic review process through the automated prioritization of relevant articles for screening. This study examined the screening efficiency of ASReview when conducting systematic reviews and the potential factors that could influence its efficiency.

Six distinct topics within the field of periodontics were searched in PubMed and Web of Science to obtain articles for screening within ASReview. Through a "training" process, relevant and irrelevant articles were manually incorporated to develop "prior knowledge" and facilitate ML optimization. Screening was then conducted following ASReview's algorithmically-generated relevance rankings. Screening efficiency was evaluated based on the normalized number of articles not requiring detailed review and on the total time expenditure.

Across the six topics, an average of 60.2 % of articles did not warrant extensive screening, given that all relevant articles were discovered within the first 39.8 % of publication reviewed. No significant variations in efficiencies were observed with differing methods of assembling prior knowledge articles or via modifications in article ratios and numbers.

On average, ASReview conferred a 60.2 % improvement in screening efficiency, largely attributed to its dynamic ML capabilities. While advanced technologies like ASReview promise enhanced efficiencies, the accurate human discernment of article relevancy and quality remains indispensable when training these AI tools.

Using ASReview has the potential to save approximately 60 % of time and effort required for screening articles.

This article examines the transformative potential of artificial intelligence (AI) in shaping the future of healthcare. It highlights AI's capacity to revolutionize various medical fields, including diagnostics, personalized treatment, drug discovery, telemedicine, and patient care management. Key areas explored include AI's roles in cancer screening, reproductive health, cardiology, outpatient care, laboratory diagnosis, language translation, neuroscience, robotic surgery, radiology, personal healthcare, patient engagement, AI-assisted rehabilitation with exoskeleton robots, and administrative efficiency. The article also addresses challenges to AI adoption, such as privacy concerns, ethical issues, cost barriers, and decision-making authority in patient care. By overcoming these challenges and building trust, AI is positioned to become a critical driver in advancing healthcare, improving outcomes, and meeting the future needs of patients and providers.

Cardiovascular diseases (CVDs) are a major cause of mortality globally, emphasizing the need for early detection and effective risk assessment to improve patient outcomes. Advances in oculomics, which utilize the relationship between retinal microvascular changes and systemic vascular health, offer a promising non-invasive approach to assessing CVD risk. Retinal fundus imaging and optical coherence tomography/angiography (OCT/OCTA) provides critical information for early diagnosis, with retinal vascular parameters such as vessel caliber, tortuosity, and branching patterns identified as key biomarkers. Given the large volume of data generated during routine eye exams, there is a growing need for automated tools to aid in diagnosis and risk prediction. The study demonstrates that AI-driven analysis of retinal images can accurately predict cardiovascular risk factors, cardiovascular events, and metabolic diseases, surpassing traditional diagnostic methods in some cases. These models achieved area under the curve (AUC) values ranging from 0.71 to 0.87, sensitivity between 71% and 89%, and specificity between 40% and 70%, surpassing traditional diagnostic methods in some cases. This approach highlights the potential of retinal imaging as a key component in personalized medicine, enabling more precise risk assessment and earlier intervention. It not only aids in detecting vascular abnormalities that may precede cardiovascular events but also offers a scalable, non-invasive, and cost-effective solution for widespread screening. However, the article also emphasizes the need for further research to standardize imaging protocols and validate the clinical utility of these biomarkers across different populations. By integrating oculomics into routine clinical practice, healthcare providers could significantly enhance early detection and management of systemic diseases, ultimately improving patient outcomes. Fundus image analysis thus represents a valuable tool in the future of precision medicine and cardiovascular health management.

Introduction: Accurate prediction of tumor dynamics following Gamma Knife radiosurgery (GKRS) is critical for optimizing treatment strategies for patients with brain metastases (BMs). Traditional machine learning (ML) algorithms have been widely used for this purpose; however, recent advancements in deep learning, such as autoencoders, offer the potential to enhance predictive accuracy. This study aims to evaluate the efficacy of autoencoders compared to traditional ML models in predicting tumor progression or regression after GKRS. Objectives: The primary objective of this study is to assess whether integrating autoencoder-derived features into traditional ML models can improve their performance in predicting tumor dynamics three months post-GKRS in patients with brain metastases. Methods: This retrospective analysis utilized clinical data from 77 patients treated at the "Prof. Dr. Nicolae Oblu" Emergency Clinic Hospital-Iasi. Twelve variables, including socio-demographic, clinical, treatment, and radiosurgery-related factors, were considered. Tumor progression or regression within three months post-GKRS was the primary outcome, with 71 cases of regression and 6 cases of progression. Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. The study further explored the impact of incorporating features derived from autoencoders, particularly focusing on the effect of compression in the bottleneck layer on model performance. Results: Traditional ML models achieved accuracy rates ranging from 0.91 (KNN) to 1.00 (Extra Trees). Integrating autoencoder-derived features generally enhanced model performance. Logistic Regression saw an accuracy increase from 0.91 to 0.94, and SVM improved from 0.85 to 0.96. XGBoost maintained consistent performance with an accuracy of 0.94 and an AUC of 0.98, regardless of the feature set used. These results demonstrate that hybrid models combining deep learning and traditional ML techniques can improve predictive accuracy. Conclusion: The study highlights the potential of hybrid models incorporating autoencoder-derived features to enhance the predictive accuracy and robustness of traditional ML models in forecasting tumor dynamics post-GKRS. These advancements could significantly contribute to personalized medicine, enabling more precise and individualized treatment planning based on refined predictive insights, ultimately improving patient outcomes.

The rapid advancement of artificial intelligence (AI) has sparked renewed discussions on its trustworthiness and the concept of eXplainable AI (XAI). Recent research in neuroscience has emphasized the relevance of XAI in studying cognition. This scoping review aims to identify and analyze various XAI methods used to study the mechanisms and features of cognitive function and dysfunction. In this study, the collected evidence is qualitatively assessed to develop an effective framework for approaching XAI in cognitive neuroscience. Based on the Joanna Briggs Institute and preferred reporting items for systematic reviews and meta-analyses extension for scoping review guidelines, we searched for peer-reviewed articles on MEDLINE, Embase, Web of Science, Cochrane Central Register of Controlled Trials, and Google Scholar. Two reviewers performed data screening, extraction, and thematic analysis in parallel. Twelve eligible experimental studies published in the past decade were included. The results showed that the majority (75%) focused on normal cognitive functions such as perception, social cognition, language, executive function, and memory, while others (25%) examined impaired cognition. The predominant XAI methods employed were intrinsic XAI (58.3%), followed by attribution-based (41.7%) and example-based (8.3%) post hoc methods. Explainability was applied at a local (66.7%) or global (33.3%) scope. The findings, predominantly correlational, were anatomical (83.3%) or nonanatomical (16.7%). In conclusion, while these XAI techniques were lauded for their predictive power, robustness, testability, and plausibility, limitations included oversimplification, confounding factors, and inconsistencies. The reviewed studies showcased the potential of XAI models while acknowledging current challenges in causality and oversimplification, particularly emphasizing the need for reproducibility.

In the last decade, artificial intelligence (AI) has significantly impacted ophthalmology, particularly in managing corneal diseases, a major reversible cause of blindness. This review explores AI's transformative role in the corneal subspecialty, which has adopted advanced technology for superior clinical judgment, early diagnosis, and personalized therapy. While AI's role in anterior segment diseases is less documented compared to glaucoma and retinal pathologies, this review highlights its integration into corneal diagnostics through imaging techniques like slit-lamp biomicroscopy, anterior segment optical coherence tomography (AS-OCT), and in vivo confocal biomicroscopy. AI has been pivotal in refining decision-making and prognosis for conditions such as keratoconus, infectious keratitis, and dystrophies. Multi-disease deep learning neural networks (MDDNs) have shown diagnostic ability in classifying corneal diseases using AS-OCT images, achieving notable metrics like an AUC of 0.910. AI's progress over two decades has significantly improved the accuracy of diagnosing conditions like keratoconus and microbial keratitis. For instance, AI has achieved a 90.7% accuracy rate in classifying bacterial and fungal keratitis and an AUC of 0.910 in differentiating various corneal diseases. Convolutional neural networks (CNNs) have enhanced the analysis of color-coded corneal maps, yielding up to 99.3% diagnostic accuracy for keratoconus. Deep learning algorithms have also shown robust performance in detecting fungal hyphae on in vivo confocal microscopy, with precise quantification of hyphal density. AI models combining tomography scans and visual acuity have demonstrated up to 97% accuracy in keratoconus staging according to the Amsler-Krumeich classification. However, the review acknowledges the limitations of current AI models, including their reliance on binary classification, which may not capture the complexity of real-world clinical presentations with multiple coexisting disorders. Challenges also include dependency on data quality, diverse imaging protocols, and integrating multimodal images for a generalized AI diagnosis. The need for interpretability in AI models is emphasized to foster trust and applicability in clinical settings. Looking ahead, AI has the potential to unravel the intricate mechanisms behind corneal pathologies, reduce healthcare's carbon footprint, and revolutionize diagnostic and management paradigms. Ethical and regulatory considerations will accompany AI's clinical adoption, marking an era where AI not only assists but augments ophthalmic care.

Artificial intelligence (AI) stands at the forefront of revolutionizing healthcare, wielding its computational prowess to navigate the labyrinth of medical data with unprecedented precision. In this study, we delved into the perspectives of medical students in the Kingdom of Saudi Arabia (KSA) regarding AI's seismic impact on their careers and the medical landscape.

 A cross-sectional study conducted from February to December 2023 examined the impact of AI on the future of medical students' careers in KSA, surveying approximately 400 participants, including Saudi medical students and interns, and uncovering a fascinating tapestry of perceptions.

Astonishingly, 75.4% of respondents boasted familiarity with AI, heralding its transformative potential. A resounding 88.9% lauded its capacity to enrich medical education, marking a paradigm shift in learning approaches. However, amidst this wave of optimism, shadows of apprehension loomed. A staggering 42.5% harbored concerns of AI precipitating job displacement, while 34.4% envisioned a future where AI usurps traditional doctor roles. Despite this dichotomy, there existed a unanimous recognition of the symbiotic relationship between AI and human healthcare professionals, heralding an era of collaborative synergy.

Our findings underscored a critical need for educational initiatives to assuage fears and facilitate the seamless integration of AI into clinical practice. Moreover, AI's burgeoning influence in diagnostic radiology and personalized healthcare plans emerged as catalysts propelling the domain of precision medicine into uncharted realms of innovation. As AI reshapes the contours of healthcare delivery, it not only promises unparalleled efficiency but also holds the key to unlocking new frontiers in treatment outcomes and accessibility, heralding a transformative epoch in the annals of medicine.

The discourse surrounding medical artificial intelligence (AI) often focuses on narratives that either hype the technology's potential or predict dystopian futures. AI narratives have a significant influence on the direction of research, funding, and public opinion and thus shape the future of medicine.

The paper aims to offer critical reflections on AI narratives, with a specific focus on medical AI, and to raise awareness as to how people working with medical AI talk about AI and discharge their "narrative responsibility."

Qualitative semistructured interviews were conducted with 41 participants from different disciplines who were exposed to medical AI in their profession. The research represents a secondary analysis of data using a thematic narrative approach. The analysis resulted in 2 main themes, each with 2 other subthemes.

Stories about the AI-physician interaction depicted either a competitive or collaborative relationship. Some participants argued that AI might replace physicians, as it performs better than physicians. However, others believed that physicians should not be replaced and that AI should rather assist and support physicians. The idea of excessive technological deferral and automation bias was discussed, highlighting the risk of "losing" decisional power. The possibility that AI could relieve physicians from burnout and allow them to spend more time with patients was also considered. Finally, a few participants reported an extremely optimistic account of medical AI, while the majority criticized this type of story. The latter lamented the existence of a "magical theory" of medical AI, identified with techno-solutionist positions.

Most of the participants reported a nuanced view of technology, recognizing both its benefits and challenges and avoiding polarized narratives. However, some participants did contribute to the hype surrounding medical AI, comparing it to human capabilities and depicting it as superior. Overall, the majority agreed that medical AI should assist rather than replace clinicians. The study concludes that a balanced narrative (that focuses on the technology's present capabilities and limitations) is necessary to fully realize the potential of medical AI while avoiding unrealistic expectations and hype.

The original COVID-19 vaccines, developed against SARS-CoV-2, initially mitigated hospitalizations. Bivalent vaccine boosters were used widely during 2022-23, but the outbreaks persisted. Despite this, hospitalizations, mortality, and outbreaks involving dominant mutants like Alpha and Delta increased during winters when the population's vitamin D levels were at their lowest. Notably, 75 % of human immune cell/system functions, including post-vaccination adaptive immunity, rely on adequate circulatory vitamin D levels. Consequently, hypovitaminosis compromises innate and adaptive immune responses, heightening susceptibility to infections and complications. COVID-19 vaccines primarily target SARS-CoV-2 Spike proteins, thus offering only a limited protection through antibodies. mRNA vaccines, such as those for COVID-19, fail to generate secretory/mucosal immunity-like IgG responses, rendering them ineffective in halting viral spread. Additionally, mutations in the SARS-CoV-2 binding domain reduce immune recognition by vaccine-derived antibodies, leading to immune evasion by mutant viruses like Omicron variants. Meanwhile, the repeated administration of bivalent boosters intended to enhance efficacy resulted in the immunoparesis of recipients. As a result, relying solely on vaccines for outbreak prevention, it became less effective. Dominant variants exhibit increased affinity to angiotensin-converting enzyme receptor-2, enhancing infectivity but reducing virulence. Meanwhile, spike protein-related viral mutations do not impact the potency of widely available, repurposed early therapies, like vitamin D and ivermectin. With the re-emergence of COVID-19 and impending coronaviral pandemics, regulators and health organizations should proactively consider approval and strategic use of cost-effective adjunct therapies mentioned above to counter the loss of vaccine efficacy against emerging variants and novel coronaviruses and eliminate vaccine- and anti-viral agents-related serious adverse effects. Timely implementation of these strategies could reduce morbidity, mortality, and healthcare costs and provide a rational approach to address future epidemics and pandemics. This perspective critically reviews relevant literature, providing insights, justifications, and viewpoints into how the scientific community and health authorities can leverage this knowledge cost-effectively.

(1) Background: Artificial intelligence (AI) has flourished in recent years. More specifically, generative AI has had broad applications in many disciplines. While mental illness is on the rise, AI has proven valuable in aiding the diagnosis and treatment of mental disorders. However, there is little to no research about precisely how much interest there is in AI technology. (2) Methods: We performed a Google Trends search for "AI and mental health" and compared relative search volume (RSV) indices of "AI", "AI and Depression", and "AI and anxiety". This time series study employed Box-Jenkins time series modeling to forecast long-term interest through the end of 2024. (3) Results: Within the United States, AI interest steadily increased throughout 2023, with some anomalies due to media reporting. Through predictive models, we found that this trend is predicted to increase 114% through the end of the year 2024, with public interest in AI applications being on the rise. (4) Conclusions: According to our study, we found that the awareness of AI has drastically increased throughout 2023, especially in mental health. This demonstrates increasing public awareness of mental health and AI, making advocacy and education about AI technology of paramount importance.

The aim of this study was to assess, through a systematic review, the status of infrared thermography (IRT) as a diagnostic tool for skin neoplasms of the head and neck region and in order to validate its effectiveness in differentiating benign and malignant lesions. A search was carried out in the LILACS, PubMed/MEDLINE, SCOPUS, Web of Science and EMBASE databases including studies published between 2004 and 2024, written in the Latin-Roman alphabet. Accuracy studies with patients aged 18 years or over presenting benign and malignant lesions in the head and neck region that evaluated the performance of IRT in differentiating these lesions were included. Lesions of mesenchymal origin and studies that did not mention histopathological diagnosis were excluded. The systematic review protocol was registered in the PROSPERO database (CRD42023416079). Reviewers independently analyzed titles, abstracts, and full-texts. After extracting data, the risk of bias of the selected studies was assessed using the QUADAS - 2 tool. Results were narratively synthesized and the certainty of evidence was measured using the GRADE approach. The search resulted in 1,587 records and three studies were included. Only one of the assessed studies used static IRT, while the other two studies used cold thermal stress. All studies had an uncertain risk of bias. In general, studies have shown wide variation in the accuracy of IRT for differentiating between malignant and benign lesions, with a low level of certainty in the evidence for both specificity and sensitivity.

Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.

This study aimed to develop and validate evaluation metric for an automated smile classification model termed the "smile index." This innovative model uses computational methods to numerically classify and analyze conventional smile types.

The datasets used in this study consisted of 300 images to verify, 150 images to validate, and 9 images to test the evaluation metric. Images were annotated using Labelme. Computational techniques were used to calculate smile index values for the study datasets, and the resulting values were evaluated in three stages.

The smile index successfully classified smile types using cutoff values of 0.0285 and 0.193. High accuracy (0.933) was achieved, along with an F1 score greater than 0.09. The smile index successfully reclassified smiles into six types (low, low-to-medium, medium, medium-to-high, high, and extremely high smiles), thereby providing a clear distinction among different smile characteristics.

The smile index is a novel dimensionless parameter for classifying smile types. The index acts as a robust evaluation tool for artificial intelligence models that automatically classify smile types, thereby providing a scientific basis for largely subjective aesthetic elements.

The computational approach employed by the smile index enables quantitative numerical classification of smile types. This fosters the application of computerized methods in quantifying and analyzing real smile characteristics observed in clinical practice, paving the way for a more objective evidence-based approach to aesthetic dentistry.

Subdural hematoma is defined as blood collection in the subdural space between the dura mater and arachnoid. Subdural hematoma is a condition that neurosurgeons frequently encounter and has acute, subacute and chronic forms. The incidence in adults is reported to be 1.72-20.60/100.000 people annually. Our study aimed to evaluate the quality, reliability and readability of the answers to questions asked to ChatGPT, Bard, and perplexity about "Subdural Hematoma." In this observational and cross-sectional study, we asked ChatGPT, Bard, and perplexity to provide the 100 most frequently asked questions about "Subdural Hematoma" separately. Responses from both chatbots were analyzed separately for readability, quality, reliability and adequacy. When the median readability scores of ChatGPT, Bard, and perplexity answers were compared with the sixth-grade reading level, a statistically significant difference was observed in all formulas (P < .001). All 3 chatbot responses were found to be difficult to read. Bard responses were more readable than ChatGPT's (P < .001) and perplexity's (P < .001) responses for all scores evaluated. Although there were differences between the results of the evaluated calculators, perplexity's answers were determined to be more readable than ChatGPT's answers (P < .05). Bard answers were determined to have the best GQS scores (P < .001). Perplexity responses had the best Journal of American Medical Association and modified DISCERN scores (P < .001). ChatGPT, Bard, and perplexity's current capabilities are inadequate in terms of quality and readability of "Subdural Hematoma" related text content. The readability standard for patient education materials as determined by the American Medical Association, National Institutes of Health, and the United States Department of Health and Human Services is at or below grade 6. The readability levels of the responses of artificial intelligence applications such as ChatGPT, Bard, and perplexity are significantly higher than the recommended 6th grade level.

Expedited implementation of evidence into practice and policymaking is critical to ensure the delivery of effective care and improve health-care outcomes. Implementation science deals with the designing of methods and strategies for increasing and facilitating the uptake of evidence into practice and policymaking. Nevertheless, the process of designing and selecting methods and strategies for implementing evidence is complicated because of the complexity of health-care settings where implementation is desired. Artificial intelligence (AI) has revolutionized a range of fields, including genomics, education, drug trials, research, and health care. This commentary discusses how AI can be leveraged to expedite implementation science efforts for transforming health-care practice. Four key aspects of AI use in implementation science are highlighted: (a) AI for implementation planning (e.g., needs assessment, predictive analytics, and data management), (b) AI for developing implementation tools and guidelines, (c) AI for designing and applying implementation strategies, and (d) AI for monitoring and evaluating implementation outcomes. Use of AI along the implementation continuum from planning to delivery and evaluation can enable more precise and accurate implementation of evidence into practice.

Background: Ideas about Artificial intelligence appeared about half a century ago, but only now is it becoming an essential element of everyday life. The data provided are becoming a bigger pool and we need artificial intelligence that will help us with its superhuman powers. Its interaction with medicine is improving more and more, with medicine being a domain that continues to be perfected. Materials and Methods: The most important databases were used to perform this detailed search that addresses artificial intelligence in the medical and surgical fields. Discussion: Machine learning, deep learning, neural networks and computer vision are some of the mechanisms that are becoming a trend in healthcare worldwide. Developed countries such as Japan, France and Germany have already implemented artificial intelligence in their medical systems. The help it gives is in medical diagnosis, patient monitoring, personalized therapy and workflow optimization. Artificial intelligence will help surgeons to perfect their skills, to standardize techniques and to choose the best surgical techniques. Conclusions: The goal is to predict complications, reduce diagnostic times, diagnose complex pathologies, guide surgeons intraoperatively and reduce medical errors. We are at the beginning of this, and the potential is enormous, but we must not forget the impediments that may appear and slow down its implementation.

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.