Evaluating the Impact of Digital Tool Utilization in Dentistry on Burnout Syndrome Among Dentists: An Entropy Analysis and AI-Driven Approach

Recent developments in the application of artificial intelligence (AI) in health care promise to solve many of the existing global problems in improving human health care and managing global legal challenges. In addition to machine learning techniques, artificial intelligence is currently being applied in health care in other forms, such as robotic systems. However, the artificial intelligence currently used in health care is not fully autonomous, given that health care professionals make the final decision. Therefore, the most prevalent legal issues relating to the application of artificial intelligence are patient safety, impact on patient-physician relationship, physician’s responsibility, the right to privacy, data protection, intellectual property protection, lack of proper regulation, algorithmic transparency and governance of artificial intelligence empowered health care. Hence, the aim of this research is to point out the possible legal consequences and challenges of regulation and control in the application of artificial intelligence in health care. The results of this paper confirm the potential of artificial intelligence to noticeably improve patient care and advance medical research, but the shortcomings of its implementation relate to a complex legal and ethical issue that remains to be resolved. In this regard, it is necessary to achieve a broad social consensus regarding the application of artificial intelligence in health care, and adopt legal frameworks that determine the conditions for its application.

Objective: To assess the knowledge and attitude of undergraduate nursing students towards the role of artificial intelligence (AI) in healthcare, aiming to understand their readiness and perception of integrating AI into clinical practice. Methods: A descriptive cross-sectional study was conducted to assess the knowledge and attitude of nursing students toward the role of Artificial Intelligence (AI) in healthcare. A total of 208 students were selected using non-probability convenience sampling technique. Informed consent was obtained from all the participants prior to the data collection. The study consisted of two parts: a 10-items knowledge questionnaire and a 10-items attitude questionnaire, designed to evaluate students' understanding of AI technologies and their perspectives on its integration into healthcare settings. The questionnaires were close-ended, focusing on basic knowledge about AI. Results: There was a significant difference in AI knowledge and attitudes between various groups. Male’s demonstrated significantly higher AI knowledge (82.1%) compared to females (69.8%) with a p-value of 0.003. Participants who attended formal AI training exhibited better knowledge, with 41.9% showing adequate knowledge, compared to 25.4% of non-attendees (p = 0.010). Prior exposure to AI workshops significantly influenced attitudes, with attendees showing a more positive attitude toward AI (67.4%) compared to non-attendees (35.8%), with a p-value of <0.001. Gender and formal AI training were found to significantly impact both knowledge and attitude towards AI in healthcare. Conclusion: The study highlights significant differences in AI knowledge and attitude among undergraduate nursing students, with males, participants with formal AI training, and those exposed to AI workshops demonstrating higher levels of knowledge and more positive attitude. These findings underscore the importance of incorporating AI education and training into nursing curricula to better prepare students for the integration of AI in clinical practice.

The integration of artificial intelligence (AI) in healthcare has the potential to revolutionize the industry by improving patient outcomes and increasing efficiency. However, the rapid development and implementation of AI technologies raise complex legal issues and challenges. This article explores the key legal aspects of AI integration in healthcare, including data privacy and security, liability and accountability, intellectual property, and regulatory compliance. It examines relevant international and national legal instruments, regulations, and guidelines, as well as industry-specific standards that apply to AI in healthcare. The study also analyzes case studies and practical applications to highlight legal challenges and resolutions, lessons learned, and best practices. The discussion addresses the implications of the results, comparing the legal landscape for AI in healthcare to other industries and countries and highlighting potential future legal developments and challenges. The conclusion summarizes key findings, offers recommendations for integrating AI in healthcare systems while addressing legal concerns, and proposes future directions for legal research and policy development in the context of AI and healthcare. This comprehensive analysis aims to inform healthcare providers, AI developers, and policymakers on the legal landscape surrounding AI in healthcare, providing valuable insights to navigate this complex domain and harness the potential of AI to transform healthcare delivery.

The Promise Fund and Hologic Inc. have partnered to expand access to AI-supported breast cancer screening exams in a medically underserved population. As part of this initiative, patient focus groups and patient navigator interviews were conducted to explore patient experiences and perceptions related to breast health, mammography, and artificial intelligence (AI) in healthcare. These interviews were held, virtually, between February and March 2024, and focus groups were held, in-person, on April 9-10, 2024, with participants at FoundCare and the Community Health Center. FoundCare, a federally qualified health center (FQHC), and Community Health Center, a free clinic, both collaborate with the Promise Fund in promoting access to women’s breast and cervical health screenings, diagnostic follow up and cancer care services. The in-person sessions, attended by twenty patients, and the virtual interviews, attended by six patient navigators, were designed to represent diverse linguistic, racial, and cultural backgrounds, including multiple Spanish-speaking communities from Mexico, Dominican Republic, Guatemala, Chile, and Venezuela. All patient navigators were adult women from various free clinics and FQHCs. Patient participants were adult females, all at or below 200% of the Federal Poverty Guidelines. Key themes from the discussions included: 1. Health Information Sources: Patients primarily relied on the internet, community health centers, medical visits, family, and cultural community communications for health information. Some patients expressed difficulty in keeping up with health information and emphasized the importance of direct medical consultations with providers and preventive care. Patient navigators were a critical source of information once the patient could access and be connected to that resource. Clinical health and more upstream social services, such as transportation to health appointments and family food security, were also critical needs that patient navigators were key in facilitating. 2. Mammogram Experiences: Experiences with mammograms varied, with some patients reporting pain and discomfort, particularly those with implants. Regular screenings, family history of breast cancer, and personal vigilance were common motivators for mammograms. Financial barriers, insurance issues, and the need for patient advocacy were frequently highlighted. 3. AI in Healthcare: Patients had mixed levels of awareness and understanding of AI. Many associated AI with advanced diagnostics and potential improvements in early disease detection and surgical precision. Concerns included the potential loss of personal interactions with healthcare providers, privacy issues, and the fear of job displacement. However, patients had key opinions on AI that were more favorable than anticipated. There was optimism about AI’s ability to enhance diagnostic accuracy, patient experience, provider trustworthiness, and treatment outcomes. Additionally, both patients and patient navigators preferred that physicians and AI work together. Patient navigators were even more familiar with AI. Given the critical role patient navigators hold, their knowledge may play an integral role in improving patient education and access to new technologies in the future. 4. Whole Health Experience: Patients expressed a desire for a more comfortable and supportive mammography process, greater access to care, enhanced trust between patients and providers, and comprehensive information sharing across healthcare providers. Education on breast cancer, early detection, and AI in healthcare was identified as a critical need. Conclusion: The findings underscore the importance of trustworthy, patient-centered approaches in healthcare, particularly in the integration of AI technologies. Enhancing patient education and provider transparency, improving access to preventive services, and maintaining the human element in healthcare are essential for optimizing patient outcomes and satisfaction. Citation Format: Nancy Brinker. Patient Experience and Perceptions Related to Breast Health, Mammography and Artificial Intelligence in Healthcare [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P3-05-11.

The development of Artificial Intelligence (AI) in healthcare has had a significant impact on healthcare. AI in healthcare can provide more accurate diagnoses and interventions for patients. AI can predict, diagnose, and treat diseases, facilitate the maximum use of healthcare resources by integrating medical information, increase efficiency, and reduce overcrowding of healthcare resources. However, the application of AI in healthcare also faces challenges such as accountability, algorithmic security, and data privacy. This paper discusses the application of AI in healthcare and explores the challenges faced by AI, in-cluding accountability traceability, algorithmic safety, data security, and ethical issues, and makes targeted recommendations. This study provides an in-depth exploration of the application of AI in healthcare, helping to improve the accuracy and efficiency of AI ap-plications in healthcare, as well as providing necessary guidance and references for opti-mizing and enhancing AI technologies.

Objectives The use of artificial intelligence (AI) in healthcare is a growing field of research and clinical application. The views of the general public, ie future healthcare users, need to be surveyed and interpreted so that researchers and the public have a shared understanding of the appropriate use of AI. Currently, there is only limited data on the public’s views. Methods An anonymous, quantitative questionnaire was administered as part of a public exhibition on AI. The questionnaire was based on previously validated questions designed to assess respondents’ views on the use of AI in healthcare. Brief demographic data were also collected. Results The population surveyed was more diverse and younger than the general UK population (65% white, 45% aged 18-29). Respondents were largely comfortable with the application of AI in healthcare: 80% felt positively about its use, 56% thought it would be safe. 70% did not feel that it would replace doctors, and most would not be happy for AI to make decisions without considering their feelings. Conclusions Our study shows that the population we surveyed, particularly young future healthcare users, are comfortable with the use of AI in healthcare, but do not see it as a replacement for doctors. Advances in knowledge This paper highlights views from the general public on the use of AI in healthcare, which is largely under researched.

This article examines the potential implications of the implementation of artificial intelligence (AI) in health care for both its delivery and the medical profession. To this end, the first section explores the basic features of AI and the yet theoretical concept of autonomous AI followed by an overview of current and developing AI applications. Against this background, the second section discusses the transforming roles of physicians and changes in the patient-physician relationship that could be a consequence of gradual expansion of AI in health care. Subsequently, an examination of the responsibilities physicians should assume in this process is explored. The third section describes conceivable practical and ethical challenges that implementation of a single all-encompassing AI healthcare system would pose. The fourth section presents arguments for regulation of AI in health care to ensure that these applications do not violate basic ethical principles and that human control of AI will be preserved in the future. In the final section, fundamental components of a moral framework from which such regulation may be derived are brought forward, and some possible strategies for building a moral framework are discussed.

ObjectivesApplications of Artificial intelligence (AI) in health care are strikingly advancing and revolutionising the patient care in the recent years. We wanted to evaluate the perception of AI by our...

This study focuses on the potential application of Artificial Intelligence (AI) in healthcare and hospitals to improve the quality of services for patients. The research objectives include the investigation of existing AI use cases in healthcare, exploration of potential areas in which AI can best be applied, and identification of the challenges to successful AI application. This research utilizes both primary and secondary data sources to investigate the potential of AI in healthcare and hospitals. The primary data is collected through published research papers, technical reports, and industry news to gain an understanding of the current state of AI applications in healthcare. The secondary data is gathered from expert opinions with experienced healthcare professionals such as physicians, hospital administrators, and IT experts to gain insights into existing use cases and potential applications of AI in healthcare and hospitals. The results of the study demonstrate that AI has a significant potential to deliver enhanced outcomes in various aspects of healthcare and hospitals, including diagnosis, treatment, and management. However, the successful integration of AI requires overcoming numerous challenges such as regulatory standardization, privacy protection, and data availability. To foster a positive development of AI in healthcare, it is recommended that healthcare organizations enhance their digital capabilities, enable secure data sharing and collaboration, and use AI tools to deliver a more comprehensive and personalized patient care experience.

Introduction: Artificial intelligence has rapidly gained importance as a transformative force in healthcare, influencing not only clinical processes but also management practices, leadership models, and strategic decision-making. This review explores the evolving role of AI in health management, focusing on its impact on institutional transformation, leadership paradigms, and strategic orientations. Methods: This article adopts a narrative literature review approach to synthesize recent theoretical and empirical studies on artificial intelligence and leadership in healthcare. Peer-reviewed studies published between 2017 and 2025 were identified through databases such as PubMed, Scopus, and Web of Science, using keywords including "artificial intelligence in healthcare," "healthcare leadership," "digital transformation," and "strategic management in healthcare." Studies were selected based on their relevance to AI's role in organizational change and leadership development in health systems. Results: The reviewed literature identifies three major themes: (1) the integration of AI in healthcare operations, including resource allocation, patient flow management, and crisis response; (2) the transformation of leadership styles from hierarchical to data-driven, agile, and ethically responsible models; and (3) the strategic positioning of AI in fostering sustainable, inclusive, and future-oriented organizational cultures. These findings suggest a shift in leadership expectations from operational control to strategic vision and ethical AI governance. Conclusion: AI is reshaping health management by enabling leaders to develop strategic foresight, support evidence-based decision-making, and drive digital transformation. The success of AI integration depends not only on technological adoption but also on ethical frameworks, organizational learning, and leadership vision. Future healthcare leaders should combine digital competencies and emotional intelligence with a human-centered approach to leadership.

BackgroundWhile use of artificial intelligence (AI) in healthcare is increasing, little is known about how patients view healthcare AI. Characterizing patient attitudes and beliefs about healthcare AI and the factors that lead to these attitudes can help ensure patient values are in close alignment with the implementation of these new technologies.MethodsWe conducted 15 focus groups with adult patients who had a recent primary care visit at a large academic health center. Using modified grounded theory, focus-group data was analyzed for themes related to the formation of attitudes and beliefs about healthcare AI.ResultsWhen evaluating AI in healthcare, we found that patients draw on a variety of factors to contextualize these new technologies including previous experiences of illness, interactions with health systems and established health technologies, comfort with other information technology, and other personal experiences. We found that these experiences informed normative and cultural beliefs about the values and goals of healthcare technologies that patients applied when engaging with AI. The results of this study form the basis for a theoretical framework for understanding patient orientation to applications of AI in healthcare, highlighting a number of specific social, health, and technological experiences that will likely shape patient opinions about future healthcare AI applications.ConclusionsUnderstanding the basis of patient attitudes and beliefs about healthcare AI is a crucial first step in effective patient engagement and education. The theoretical framework we present provides a foundation for future studies examining patient opinions about applications of AI in healthcare.

Objective. To review the literature related to ethics of artificial intelligence (AI) in healthcare, with a particular emphasis on its challenges and implications in nursing. Methods. Data bases including PubMed, Scopus, Web of Science, and CINAHL are reviewed. Inclusion criteria focused on English-language articles addressing AI ethics in healthcare, with priority given to empirical studies, World Health Organization (WHO) reports, and nursing-specific scholarship. General Search Items included artificial intelligence ethics, AI in healthcare challenges, nursing AI implications, algorithmic bias healthcare, informed consent AI, privacy data protection AI, and WHO AI guidelines, combined with Boolean operators (e.g., "AI AND nursing autonomy") and filters for publication date (post-2018) and article type (reviews, originals). Results. Most of the studies emphasizes that integration of Artificial intelligence provides substantial benefits for patients, medical professionals, and the overall healthcare framework. Like the improving the primary healthcare, cost reduction, and enhanced efficiency of medical and clinical processes and it also helps where human intelligence is needed i.e. analytical reasoning, acquiring knowledge, and decision-making. While it offers immense possibilities, this technology demands vast amounts of patient information, leading to concerns about confidentiality, protection, and other moral dilemmas. It also highlights the need for nurses to develop AI literacy and bias recognition to balance technological efficiency with humanistic care and ethical evaluation; enabling nurses to monitor unethical AI applications and ensure fairness in patient care. Conclusion. AI is revolutionizing the healthcare sector but demands robust ethical governance to mitigate harms like discrimination and privacy erosion. For nursing, proactive integration-via updated curricula and interdisciplinary policies-can foster safe, equitable AI adoption, ultimately advancing human dignity and health outcomes.

The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned

It is widely accepted that explainability is a requirement for the ethical use of artificial intelligence (AI) in health care. I challenge this Explainability Imperative (EI) by considering the following question: does the use of epistemically opaque medical AI systems violate existing legal standards for informed consent? If yes, and if the failure to meet such standards can be attributed to epistemic opacity, then explainability is a requirement for AI in healthcare. If not, then based on at least one metric of ethical medical practice (informed consent), explainability is not required for the ethical use of AI in healthcare. First, I show that the use of epistemically opaque AI applications is compatible with meeting accepted legal criteria for informed consent. Second, I argue that human experts are also black boxes with respect to the criteria by which they arrive at a diagnosis. Human experts can nonetheless meet established requirements for informed consent. I conclude that the use of black-box AI systems does not violate patients’ rights to informed consent, and thus, with respect to informed consent, explainability is not required for medical AI.

Patients and members of the public are the end users of healthcare, but little is known about their views on the use of artificial intelligence (AI) in healthcare, particularly in the Japanese context. This paper reports on an exploratory two-part workshop conducted with members of a Patient and Public Involvement Panel in Japan, which was designed to identify their expectations and concerns about the use of AI in healthcare broadly. 55 expectations and 52 concerns were elicited from workshop participants, who were then asked to cluster and title these expectations and concerns. Thematic content analysis was used to identify 12 major themes from this data. Participants had notable expectations around improved hospital administration, improved quality of care and patient experience, and positive changes in roles and relationships, and reductions in costs and disparities. These were counterbalanced by concerns about problematic changes to healthcare and a potential loss of autonomy, as well as risks around accountability and data management, and the possible emergence of new disparities. The findings reflect participants' expectations for AI as a possible solution for long-standing issues in healthcare, though their overall balanced view of AI mirrors findings reported in other contexts. Thus, this paper offers initial, novel insights into perspectives on AI in healthcare from the Japanese context. Moreover, the findings are used to argue for the importance of involving patient and public stakeholders in deliberation on AI in healthcare.