Healthcare Experts Research Articles

Developing a Knowledge-Based Online Recommendation System to Guide STD Patients Through Their Journey

Sexually transmitted diseases (STDs) significantly impact public health, affecting one in five U.S. adults and imposing substantial economic burdens. Many at-risk individuals seek information and support online rather than through regular testing, facing challenges due to fragmented information and a lack of personalized recommendations. This study develops an online health recommendation system (OHRS) tailored for STD patients, integrating informational and emotional support based on their disease journey. Using a BERT-based named entity recognition (NER) algorithm, the system identifies patient emotions and stages from online posts, providing relevant support resources. Data collection for designing a recommendation engine included analyzing online posts and consulting healthcare experts. The system's effectiveness was validated through user-based simulation studies. Key contributions include developing text-mining algorithms, creating a knowledge-based recommendation system, and proposing design principles for AI-driven support systems for stigmatized conditions.

Cross-Sector Support for a Policy Framework to Tackle Social Determinants of Health.

This JAMA Forum discusses 4 focus areas developed by a collaborative of more than 40 organizations and experts in health care, social services, and other sectors designed to find ways to support and address social determinants of health with better program coordination and partnerships and novel financing approaches.

"How can we help you?": results of a scoping review on the perceived needs of people living with chronic pain regarding physiotherapy.

Physiotherapy is effective to reduce pain and improve the quality of life of people living with chronic pain. To offer high-quality physiotherapy services, these services must be patient-centred and respond to patients' needs. However, few studies seem to target patients' perceived needs, whereas more studies tend to focus on needs assessed by healthcare experts, which are not always in line with patients' perceived needs. In addition, people living with chronic pain are often faced with several health inequities and may have varied perceived needs depending on their personal conditions. To offer services that truly meet patients' needs, it is therefore crucial to understand these needs. This scoping review aims to identify and map the perceived needs of people living with chronic pain towards physiotherapy services. To conduct this review, we followed the six stages framework proposed by Arksey and O'Malley. We searched four databases (Medline, Embase, CINHAL and APA PsycINFO) as well as the grey literature. We included all studies describing the needs, demands, preferences or expectations of adults living with chronic pain towards physiotherapy. We then performed an inductive thematic analysis of the results and discussion sections of these studies to identify the perceived needs. Once those needs were identified, we mapped them into the seven dimensions of the patient-centred healthcare delivery framework. Our review included 96 studies. Various perceived needs were identified through the thematic analysis, such as the needs for an empathetic relationship; for a clear, adapted and supervised exercise program; and for personalized treatment. Our mapping into the patient-centred healthcare delivery framework showed that most studies reported needs associated with the dimensions of interpersonal care, individualized healthcare and professional care. Needs associated with the other dimensions of the framework (access; coordination and continuity; services and facilities; data and information) were less frequently mentioned. The results of this review have enabled us to identify and better understand multiple needs perceived by people living with chronic pain regarding physiotherapy services. The perceived needs identified through this scoping review were mapped within the seven dimensions of the Patient-centred healthcare delivery framework.

Knowledge, attitudes and practices regarding medication splitting and crushing among the general public in Jordan: a cross-sectional study

BackgroundSplitting or crushing medications are used for dosage administration when a certain dose is not easily accessible. Understanding the splitting or crushing of medications is essential to guarantee safe medication...

Telehealth Research Strategies in a Post COVID-19 Era: A Roadmap for the Coming Decade.

An Agency for Healthcare Research and Quality-funded Think Tank, held at Michigan State University, brought together a wide range of subject matter experts in telemedicine, telehealth, digital health, digital access, and health care. The authors of this article represented a group focused on the research needs, constructs, and strategies in the post-COVID era. While telemedicine and telehealth grew exponentially during the pandemic, the challenges that have been with us for decades, while ameliorated to some extent, remain. To showcase the State of Michigan as a model, the authors reviewed challenges and opportunities related to telehealth and telemedicine usage and categorized them into seven areas of highest priority. Based on a review of the literature and consultation in the fields of telehealth, telemedicine, and digital access, we identified seven key categories where research would be most effective going forward. These categories include research into the impact of telehealth on clinical services, telehealth's use in administrative activities, education and training for health care providers and patients, telehealth policy implications at state and federal levels, the impact of future technology and innovation on telehealth services, patient characteristics and their experiences, and the ethical aspects of telehealth in the future. We have formulated this overview of our findings to act as a roadmap for future telehealth research. We recommend that ongoing studies should explore each of the seven categories identified above. The search for solutions to overcome the challenges within these topics must not be constrained to research that has been conducted recently; many of these challenges have faced telehealth researchers for decades, and numerous solutions have been proposed over the years. Some of these proposals should be explored once again in light of technological and societal advances. The findings from these studies should be shared in ways and through venues that can make them accessible outside the spheres of academic research, but also by practitioners, policymakers, and patients. Third, research into telehealth in all its incarnations must be prioritized, and leadership is needed to ensure these areas of study are continued to be spotlighted after the noise of the COVID-19 pandemic grows quiet.

Understanding We Can Do This COVID-19 vaccine campaign elements through message frames and emotional appeals

This study looks into the message frames, emotional appeals, and approaches used in the We Can Do This COVID-19 vaccine campaign by the U.S. Department of Health and Human Services (HHS). A quantitative content analysis of 145 audio-visual campaign elements was guided by framing theory. Results indicate that the campaign messages primarily focused on vaccine benefits and used an informational approach to messaging. Consistent use of hope is observed alongside frames, such as vaccine information, facts and findings, and preventive measures. The moral obligation frame uses a more negative appeal (i.e., guilt) across the campaign elements. Additionally, while informational messages incorporate a more fear appeal, sadness, guilt, and hope remain the consistent emotional appeals in the transformational message approach. The findings of this study can serve as a valuable resource for the U.S. Government authorities and other healthcare experts in the implementation of comparable vaccination campaigns.

Vaccination as an element of health security management - analysis of factors of patient attitudes towards COVID-19 preventive vaccinations based on the opinions of healthcare experts

Background: Preventive vaccinations play a significant role in reducing the spread of infectious diseases. They mitigate the course of infectious diseases and limit the occurrence thereof. Thus, they allow maintaining the health security of citizens and are one of the activities that allow for effective population health management. However, even though vaccinations bring significant benefits to the population as a whole, the attitudes of individuals to them raise a number of controversies. Objective: The aim of the study was to analyze the characteristics and factors of patient attitudes towards COVID-19 preventive vaccinations based on the opinions of healthcare experts. The study was carried out in the framework of a project "The interdependence of lifestyle and attitudes towards COVID-19 preventive vaccinations", which finished in June 2023. Method: The research method was an in-depth telephone interview with experts who had previously studied the research problem and the results of quantitative research, and expressed individual opinions on the factors of patient attitudes towards COVID-19 vaccinations. The experts indicated various factors which, in their opinion, can influence patient willingness to be vaccinated against COVID-19. In the research process, four main factors were found: patient social environment and patient experience; patient knowledge and awareness, social impact (message of medical authorities and political decision makers), balance of benefits and risks. Results: The results obtained in the study and the conclusions drawn show the need for further, in-depth research in patient attitudes towards preventive vaccinations, not only COVID-19 vaccinations.

Patient-centred composite scores as tools for assesment of response to biological therapy for paediatric and adult severe asthma.

We have previously developed Core Outcome Measures sets for Severe Asthma (COMSA) by multi-stakeholder consensus. There are no patient-centred tools to quantify response to biologics for severe asthma. We aimed to develop paediatric and adult CompOsite iNdexes For Response in asthMa (CONFiRM) incorporating clinical parameters and patient-reported quality of life (QoL). International expert healthcare professionals (HCPs) and patients with severe asthma were invited to: 1) develop consensus levels of clinically relevant changes for each outcome measure within COMSA; 2) use multicriteria decision analysis to develop the CONFiRM scores and 3) assess their internal validity. A separate group of HCPs evaluated CONFiRM's external validity. Five levels of change for each COMSA outcome were agreed. Severe exacerbations and maintenance oral corticosteroids use were rated as most important in determining both paediatric and adult CONFiRM scores. There was strong agreement between HCPs and patients, although patients assigned greater importance to QoL. The CONFiRM score quantified response to a biological from -31 (deterioration) to 69 (best possible response). Paediatric and adult CONFiRMs had good discriminative ability for a sufficient (AUC≥0.92) and a substantial (AUC≥0.95) response to biologics. Both CONFiRMs demonstrated excellent external validity (Spearman correlation coefficients 0.9 and 0.8 for paediatric and adult respectively (p<0.0001)). We have developed novel patient-centred paediatric and adult CONFiRMs which include QoL measures. CONFiRMs should allow a more holistic understanding of response for the patient and a standardised assessment of the effectiveness of biologics between studies. Further research is needed to prospectively validate CONFiRM scores.

ChatGPT as a Source for Patient Information on Patellofemoral Surgery—A Comparative Study Amongst Laymen, Doctors, and Experts

Introduction: In November 2022, OpenAI launched ChatGPT for public use through a free online platform. ChatGPT is an artificial intelligence (AI) chatbot trained on a broad dataset encompassing a wide range of topics, including medical literature. The usability in the medical field and the quality of AI-generated responses are widely discussed and are the subject of current investigations. Patellofemoral pain is one of the most common conditions among young adults, often prompting patients to seek advice. This study examines the quality of ChatGPT as a source of information regarding patellofemoral conditions and surgery, hypothesizing that there will be differences in the evaluation of responses generated by ChatGPT between populations with different levels of expertise in patellofemoral disorders. Methods: A comparison was conducted between laymen, doctors (non-orthopedic), and experts in patellofemoral disorders based on a list of 12 questions. These questions were divided into descriptive and recommendatory categories, with each category further split into basic and advanced content. Questions were used to prompt ChatGPT in April 2024 using the ChatGPT 4.0 engine, and answers were evaluated using a custom tool inspired by the Ensuring Quality Information for Patients (EQIP) instrument. Evaluations were performed independently by laymen, non-orthopedic doctors, and experts, with the results statistically analyzed using a Mann–Whitney U Test. A p-value of less than 0.05 was considered statistically significant. Results: The study included data from seventeen participants: four experts in patellofemoral disorders, seven non-orthopedic doctors, and six laymen. Experts rated the answers lower on average compared to non-experts. Significant differences were observed in the ratings of descriptive answers with increasing complexity. The average score for experts was 29.3 ± 5.8, whereas non-experts averaged 35.3 ± 5.7. For recommendatory answers, experts also gave lower ratings, particularly for more complex questions. Conclusion: ChatGPT provides good quality answers to questions concerning patellofemoral disorders, although questions with higher complexity were rated lower by patellofemoral experts compared to non-experts. This study emphasizes the potential of ChatGPT as a complementary tool for patient information on patellofemoral disorders, although the quality of the answers fluctuates with the complexity of the questions, which might not be recognized by non-experts. The lack of personalized recommendations and the problem of “AI hallucinations” remain a challenge. Human expertise and judgement, especially from trained healthcare experts, remain irreplaceable.

Lessons Learned From Extracorporeal Life Support Practice and Outcomes During the COVID-19 Pandemic.

Extracorporeal membrane oxygenation is increasingly being used to support patients with hypoxemic respiratory failure and cardiogenic shock. During the COVID-19 pandemic, consensus guidance recommended extracorporeal life support for patients with COVID-19-related cardiopulmonary disease refractory to optimal conventional therapy, prompting a substantial expansion in the use of this support modality. Extracorporeal membrane oxygenation was particularly integral to the bridging of COVID-19 patients to heart or lung transplantation. Limited human and physical resources precluded widespread utilization of mechanical support during the COVID-19 pandemic, necessitating careful patient selection and optimal management by expert healthcare teams for judicious extracorporeal membrane oxygenation use. This review outlines the evidence supporting the use of extracorporeal life support in COVID-19, describes the practice and outcomes of extracorporeal membrane oxygenation for COVID-19-related respiratory failure and cardiogenic shock, and proposes lessons learned for the implementation of extracorporeal membrane oxygenation as a bridge to transplantation in future public health emergencies.

Use of Technology in the Management and Treatment of Prediabetes, A New Era: A Case Report Series

Introduction: The escalating prevalence of prediabetes has positioned it as a silent precursor to a looming public health crisis, particularly as it paves the way for type 2 diabetes mellitus (T2DM). This case series report explores the transformative role of technology, specifically the Sugar Fit’s Diabetes Reversal and Management Program (SDRMP), in the treatment and management of prediabetic patients. Methods: The case series follows the progress of two patients aged 31-44 with prediabetes utilizing the SDRMP to prevent the progression to diabetes. The interventions for both patients involved continuous glucose monitoring, personalized nutrition plans, fitness strategies, and behavioral modifications. The patients also engaged in regular communication with their life coaches through the Sugar Fit application, ensuring ongoing support and adherence to the program. Results: The results revealed significant improvements in glycemic control, lipid profiles, and overall well-being. The first patient, a 44-year-old male, demonstrated a successful return to normal glycemic control in 177 days through a comprehensive approach involving dietary changes, exercise, and lifestyle modifications. The second patient, a 31-year-old male, achieved normal glycemic control in a comparable timeframe, showcasing the effectiveness of the SDRMP. Discussion and Conclusion: These cases demonstrate that patients were able to adhere to the goals of care, preventing the progression of prediabetes to diabetes and improving insulin resistance. The study also underscores the potential impact of technology on public health outcomes, bridging the gap between traditional interventions and the evolving needs of a tech-savvy population. The synergy between healthcare expertise and technological innovation not only arrests the progression of prediabetes but also contributes to a broader societal shift towards metabolic well-being, paving the way for a healthier future. Future monitoring is essential to ensure the maintenance of lifestyle modifications and continued success in preventing diabetes.

Advancing EEG prediction with deep learning and uncertainty estimation

Deep Learning (DL) has the potential to enhance patient outcomes in healthcare by implementing proficient systems for disease detection and diagnosis. However, the complexity and lack of interpretability impede their widespread adoption in critical high-stakes predictions in healthcare. Incorporating uncertainty estimations in DL systems can increase trustworthiness, providing valuable insights into the model’s confidence and improving the explanation of predictions. Additionally, introducing explainability measures, recognized and embraced by healthcare experts, can help address this challenge. In this study, we investigate DL models’ ability to predict sex directly from electroencephalography (EEG) data. While sex prediction have limited direct clinical application, its binary nature makes it a valuable benchmark for optimizing deep learning techniques in EEG data analysis. Furthermore, we explore the use of DL ensembles to improve performance over single models and as an approach to increase interpretability and performance through uncertainty estimation. Lastly, we use a data-driven approach to evaluate the relationship between frequency bands and sex prediction, offering insights into their relative importance. InceptionNetwork, a single DL model, achieved 90.7% accuracy and an AUC of 0.947, and the best-performing ensemble, combining variations of InceptionNetwork and EEGNet, achieved 91.1% accuracy in predicting sex from EEG data using five-fold cross-validation. Uncertainty estimation through deep ensembles led to increased prediction performance, and the models were able to classify sex in all frequency bands, indicating sex-specific features across all bands.

Block Medcare: Advancing Healthcare Through Blockchain Integration

In an era driven by information exchange, transparency and security hold crucial importance, particularly within the healthcare industry, where data integrity and confidentiality are paramount. This paper investigates the integration of blockchain technology in healthcare, focusing on its potential to revolutionize Electronic Health Records (EHR) management and data sharing. By leveraging Ethereum-based blockchain implementations and smart contracts, we propose a novel system that empowers patients to securely store and manage their medical data. Our research addresses critical challenges in implementing blockchain in healthcare, including scalability, user privacy, and regulatory compliance. We propose a solution that combines digital signatures, Role-Based Access Control, and a multi-layered architecture to enhance security and ensure controlled access. The system's key functions, including user registration, data append, and data retrieval, are facilitated through smart contracts, providing a secure and efficient mechanism for managing health information. To validate our approach, we developed a decentralized application (dApp) that demonstrates the practical implementation of our blockchain-based healthcare solution. The dApp incorporates user-friendly interfaces for patients, doctors, and administrators, showcasing the system's potential to streamline healthcare processes while maintaining data security and integrity. Additionally, we conducted a survey to gain insights into the perceived benefits and challenges of blockchain adoption in healthcare. The results indicate strong interest among healthcare professionals and IT experts, while also highlighting concerns about integration costs and technological complexity. Our findings underscore the transformative potential of blockchain technology in healthcare, pointing towards a new era of patient-centric and secure healthcare services. By addressing current limitations and exploring future enhancements, such as integration with IoT devices and AI-driven analytics, this research contributes to the ongoing evolution of secure, efficient, and interoperable healthcare systems.

Pediatric wrist fractures: variations in management across countries. An evidence-based summary of evidence.

Fractures of the distal radius are common in pediatric population, with considerable variation in the management of pediatric wrist fractures across different countries. It is crucial to consider the different approaches to conservative management and surgical intervention. The decision on the appropriate treatment method often depends on the type and severity of the fracture, as well as the available healthcare resources and expertise in each country. This article tries to identify these variations, so the various healthcare systems can work toward implementing best practices in the management of pediatric wrist fractures on a global scale. Published peer-reviewed articles identified in electronic databases, including PubMed Scopus and Google Scholar. The management of pediatric wrist fractures can differ significantly among countries given the high variability in healthcare resources and cultural practices. The management of pediatric wrist fractures can be challenging in certain countries, especially in developing regions with limited resources. Challenges such as long therapeutic delays, lack of appropriate anesthesia, and the absence of fluoroscopy can complicate the treatment process. Randomized controlled clinical trials (RCTs) are vital in providing high-quality evidence to guide clinical decision-making, especially in the field of pediatric wrist fractures. Efforts to support and prioritize the conduct and dissemination of RCTs in pediatric wrist fracture management can ultimately lead to more consistent, effective, and evidence-based care for children with wrist fractures worldwide.

Health Data Exchange: A Search Engine to Identify Health Experts

ABSTRACTThe study proposes and describes a novel system called the Health Data Exchange. HDX is useful for identifying relevant experts in healthcare based on specific skill sets and their cohorts. Initial experience suggests that an expert database based on biographical data can be developed. Several techniques for automatically identifying the critical data, data normalization, storage, and retrieval are described that supported the development of the system. Such a system has the potential to facilitate efficient search and foster global collaboration among healthcare experts.

Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

Which factors influence the decision of hospitals to provide procedures on an outpatient basis? –Mixed-methods evidence from Germany

Shifting care from the resource-intensive inpatient setting to the more economically efficient outpatient sector is being promoted internationally by policymakers. Financial incentives are a major focus of such efforts because low levels of hospital outpatient care are attributed to differences in payment for inpatient and outpatient services. In Germany, however, there is significant variation in the extent to which hospitals provide outpatient care despite a uniform payment system. Therefore, other factors must be influencing German hospitals’ strategic decisions whether to offer outpatient care. While most research has focused on specific procedures or lacks empirical support, our study provides a comprehensive analysis of the factors beyond financial incentives that influence the provision of hospital outpatient services in Germany. We employed a mixed-methods approach, first contacting health care experts with in-depth knowledge of the hospital outpatient landscape to identify possible influencing factors and then conducting a comprehensive quantitative analysis of all German hospitals. Our findings suggest that policymakers seeking to promote hospital outpatient care should consider a broad range of factors. We found that a hospital's service mix, size, procedure volume, and emergency care infrastructure significantly affected the proportion of outpatient services it offered. Strategic hospital planning emphasizing specialization and adherence to minimum volume standards might therefore be a valuable policy tool. Our analysis also highlights the importance of demographic and socioeconomic factors, such as the regional share of single-person households, suggesting that a comprehensive policy framework should account for broader population characteristics and not just elements directly related to hospital care.

Enhancing ROP plus form diagnosis: An automatic blood vessel segmentation approach for newborn fundus images

BackgroundROP Plus Form is an eye disease that can lead to blindness, and diagnosing it requires medical experts to manually examine the retinal condition. This task is challenging due to its subjective nature and poor image quality. Therefore, developing automatic tools for Retinal Blood Vessel Segmentation in fundus images could assist healthcare experts in diagnosing, monitoring, and prognosing the disease. ObjectiveThis study focuses on developing a novel pipeline for automatically segmenting retinal blood vessels. The main requirements are that it can correctly identify the blood vessels in fundus images and perform well on different systems used for newborn evaluation. MethodsThe pipeline uses different methods, including CIELAB Enhancement, Background Normalization, Bell-Shaped Gaussian Matched Filtering, Modified Top-Hat operation, and a combination of vesselness filtering composed of Frangi and Jerman Filters. The segmentation is done by determining a threshold using the Triangle Threshold algorithm. A novel filter is also proposed to remove the Optical Disc artifacts from the primary segmentation based on the Circular Hough Transform. The segmentation pipeline is combined with different pre-trained Convolution Neural Network architectures to evaluate its automatic classification capabilities. ResultsThe pipeline was tested with newborn fundus images acquired with Clarity RetCam3 and Phoenix ICON systems. The results were compared against annotations from three ophthalmologic experts. Clarity RetCam3 images achieved an accuracy of 0.94, specificity of 0.95, and sensitivity of 0.81, while Phoenix ICON images achieved an accuracy of 0.94, specificity of 0.97, and sensitivity of 0.83. The pipeline was also tested for the DRIVE Database, achieving an accuracy of 0.95, specificity of 0.97, and sensitivity of 0.82. For the classification task, the best results were achieved with the DenseNet121 architecture with an accuracy of 0.946. ConclusionThe segmentation scores were auspicious and confirmed the clinical relevance of the proposed pipeline. It has also proven to have a good generalization performance, essential for easier clinic integration. Finally, preliminary results on using CNNs showed how our work can be used to develop fully automatic tools for diagnosing ROP Plus form disease.

Risk perceptions of healthcare workers and occupational health experts on psychological distress: a qualitative mental model study

Abstract Background The objectives of this study are to explore healthcare workers’ (HCW) mental models regarding psychological distress and to compare these with that of experts. Methods Semi-structured interviews was conducted (n = 28 HCWs, n = 13 experts). Topic list encompassed risk perception, early stress symptoms, causes, consequences and preventive measures of psychological distress. Interviews were transcribed verbatim and thematically analyzed using MAXQDA. Results Similarities were found in risk perception, symptoms, causes, and consequences. Differences arose in HCWs' reliance on personal experiences and values, versus experts' scientific perspective. Preventive measures also showed discrepancies. Variation within HCWs was found on all aspects of their mental model. Conclusion For effective preventive interventions regarding psychological distress, experts should consider HCWs' personal values and experiences, acknowledging the variation in their mental models. This approach may enhance HCWs' engagement in preventive behaviors.

Advancing Medical Image Analysis: The Role of Adaptive Optimization Techniques in Enhancing COVID-19 Detection, Lung Infection, and Tumor Segmentation

Artificial intelligence (AI) holds significant potential to revolutionize healthcare by improving clinical practices and patient outcomes. This research explores the integration of AI in healthcare, focusing on methodologies such as machine learning, natural language processing, and computer vision, which enable the extraction of valuable insights from complex medical imaging and clinical data. Through a comprehensive literature review, the study highlights AI’s practical applications in diagnostics, treatment planning, and predicting patient outcomes. Additionally, ethical issues, data privacy, and legal frameworks are examined, emphasizing the importance of responsible AI usage in healthcare. The findings demonstrate AI’s ability to enhance diagnostic accuracy, streamline administrative tasks, and optimize resource allocation, leading to personalized treatments and more efficient healthcare management. However, challenges remain, including data quality, algorithm transparency, and ethical concerns, which must be addressed to ensure safe and effective AI deployment. Continued research, collaboration between healthcare professionals and AI experts, and the development of robust regulatory frameworks are essential for maximizing AI’s benefits while minimizing risks. This research underscores the transformative potential of AI in healthcare and stresses the need for a multidisciplinary approach to address the ethical and regulatory complexities involved in its widespread adoption