The AI Doctor Will See You Now: Public Perspectives on Artificial Intelligence in Healthcare

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.

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.

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.

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.

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.

Artificial intelligence (AI) is increasingly being used in the field of healthcare to improve the efficiency and accuracy of medical diagnoses, treatment plans, and decisionmaking.1 It has the potential to transform the way healthcare is delivered and improve patient outcomes. There are many examples of the use of artificial intelligence (AI) in healthcare. One way that AI is being used in healthcare is by developing machine learning algorithms that can analyze vast amounts of patient data and identify patterns and trends that may not be immediately apparent to humans.2 This can be particularly useful in identifying early signs of diseases or conditions, allowing for earlier diagnosis, treatment, prognosis evaluation, and more. Another application of AI in healthcare is through the use of natural language processing (NLP). NLP systems can analyze electronic medical records (EMR) and extract important information, allowing healthcare providers to access and interpret patient data conveniently.3 This can help healthcare providers access and interpret patient data more easily, leading to more informed decision-making and better patient care. AI can also be used to assist with tasks such as image analysis, allowing for more accurate analysis and efficient diagnosis of medical images such as CT scans or X-rays.4 In addition, AI can be used to help automate routine tasks, freeing up healthcare providers to focus on more complex and higher-level tasks that require human expertise.5 Machine learning algorithms can analyze large amounts of patient data and identify patterns and trends that may not be immediately apparent to humans.6 This can be useful in identifying early signs of diseases or conditions, leading to earlier diagnosis and treatment. AI-powered chatbots or virtual assistants can help with tasks such as appointment scheduling and medication reminders.7 Systems that can assist with the automation of routine tasks, freeing up healthcare providers to focus on more complex and higher-level tasks that require human expertise. Predictive analytics systems can forecast patient outcomes and help healthcare providers make informed decisions about treatment plans.8 Personalized medicine systems can help tailor treatment plans to individual patients based on their specific needs and characteristics. These are just a few examples of the many ways in which AI is being used in healthcare. It is important to note that AI in healthcare is still in its early stages, and the potential applications of the technology are likely to expand significantly in the coming years.

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.

Background: With the availability of extensive health data, artificial intelligence has an inordinate capability to expedite medical explorations and revamp healthcare.Artificial intelligence is set to reform the practice of medicine soon. Despite the mammoth advantages of artificial intelligence in the medical field, there exists inconsistency in the ethical and legal framework for the application of AI in healthcare. Although research has been conducted by various medical disciplines investigating the ethical implications of artificial intelligence in the healthcare setting, the literature lacks a holistic approach. Objective: The purpose of this review is to ascertain the ethical concerns of AI applications in healthcare, to identify the knowledge gaps and provide recommendations for an ethical and legal framework. Methodology: Electronic databases Pub Med and Google Scholar were extensively searched based on the search strategy pertaining to the purpose of this review. Further screening of the included articles was done on the grounds of the inclusion and exclusion criteria. Results: The search yielded a total of 1238 articles, out of which 16 articles were identified to be eligible for this review. The selection was strictly based on the inclusion and exclusion criteria mentioned in the manuscript. Conclusion: Artificial intelligence (AI) is an exceedingly puissant technology, with the prospect of advancing medical practice in the years to come. Nevertheless, AI brings with it a colossally abundant number of ethical and legal problems associated with its application in healthcare. There are manifold stakeholders in the legal and ethical issues revolving around AI and medicine. Thus, a multifaceted approach involving policymakers, developers, healthcare providers and patients is crucial to arrive at a feasible solution for mitigating the legal and ethical problems pertaining to AI in healthcare.

BackgroundArtificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector.MethodsA cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained.ResultsFemale participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery.ConclusionThis study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.

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 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.

Purpose (stating the main purposes and research question) Anthropogenic resource use contributes to pollution, violent conflict over scarce resources, loss of biodiversity, and diminished quality of life for humans. Moreover, the “safe” amount of carbon dioxide—350 parts per million—has been exceeded. The health care industry is responsible for 4–5% of total world emissions,[i] which is similar to the global food sector.[ii] Health care carbon emissions come from health care infrastructures, supply chains and health care delivery. Increasingly, health care delivery is reliant on technologies which require the use of artificial intelligence to provide supportive care, such as triage algorithms, electronic patient records, and robotics.[iii] While these technological innovations have advanced health care significantly, they also contribute to the negative effects on the environment, among others, through carbon emissions. The environmental impacts of artificial intelligence (AI) in health care—in particular—are understudied. This research seeks to fill this gap. Methods Our team ran an exploratory search in Scopus and PubMed to identify studies that integrate environmental sustainability, artificial intelligence, and health. Results Our research initially yielded 735 studies. 77 of these studies focused on an environmental concern of a health technology or AI-application in a health care setting, but most of the articles in this subset addressed lowering energy consumption of a specific technology, such as a sensor or monitoring technology. Conclusions While there have been studies looking at AI in health care; sustainability in AI; and sustainability in health care, little attention has been paid to the interface between all three. [i] Karliner, J., Slotterback, S., Boyd, R., Ashby, B., & Steele, K. 2019. Health Care’s Climate Footprint: How the Health Sector Contributes to the Global Climate Crisis and Opportunities for Action Healthcare Without HarmARUP; September. [ii] Pichler, P. P., Jaccard, I. S., Weisz, U., & Weisz, H. 2019 International Comparison of Health Care Carbon Footprints, Environmental Research Letters 14, no. 6: 064004. [iii] Khaliq, Abdul, Ali Waqas, Qasim Ali Nisar, Shahbaz Haider, and Zunaina Asghar. 2022. Application of AI and robotics in hospitality sector: A resource gain and resource loss perspective. Technology in Society 68: 101807.

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.

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.