Artificial Intelligence in Health Care

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.

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 implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

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.

BackgroundThis paper reviews nationally representative public opinion surveys on artificial intelligence (AI) in the United States, with a focus on areas related to health care. The potential health applications of AI continue to gain attention owing to their promise as well as challenges. For AI to fulfill its potential, it must not only be adopted by physicians and health providers but also by patients and other members of the public.ObjectiveThis study reviews the existing survey research on the United States’ public attitudes toward AI in health care and reveals the challenges and opportunities for more effective and inclusive engagement on the use of AI in health settings.MethodsWe conducted a systematic review of public opinion surveys, reports, and peer-reviewed journal articles published on Web of Science, PubMed, and Roper iPoll between January 2010 and January 2022. We include studies that are nationally representative US public opinion surveys and include at least one or more questions about attitudes toward AI in health care contexts. Two members of the research team independently screened the included studies. The reviewers screened study titles, abstracts, and methods for Web of Science and PubMed search results. For the Roper iPoll search results, individual survey items were assessed for relevance to the AI health focus, and survey details were screened to determine a nationally representative US sample. We reported the descriptive statistics available for the relevant survey questions. In addition, we performed secondary analyses on 4 data sets to further explore the findings on attitudes across different demographic groups.ResultsThis review includes 11 nationally representative surveys. The search identified 175 records, 39 of which were assessed for inclusion. Surveys include questions related to familiarity and experience with AI; applications, benefits, and risks of AI in health care settings; the use of AI in disease diagnosis, treatment, and robotic caregiving; and related issues of data privacy and surveillance. Although most Americans have heard of AI, they are less aware of its specific health applications. Americans anticipate that medicine is likely to benefit from advances in AI; however, the anticipated benefits vary depending on the type of application. Specific application goals, such as disease prediction, diagnosis, and treatment, matter for the attitudes toward AI in health care among Americans. Most Americans reported wanting control over their personal health data. The willingness to share personal health information largely depends on the institutional actor collecting the data and the intended use.ConclusionsAmericans in general report seeing health care as an area in which AI applications could be particularly beneficial. However, they have substantial levels of concern regarding specific applications, especially those in which AI is involved in decision-making and regarding the privacy of health information.

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.

This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched (Scopus, Google Scholar, Philpapers, Web of Science, Pub Med), in April 2019, to support the following research question: “how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be ‘ethically mindful?’”. A series of screening stages were carried out—for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)—yielding a total of 156 papers that were included in the review.We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. This article contributes to the debate on AI in health care by offering a comprehensive analysis of the relevant literature, focusing on the ethical implications for individuals, interpersonal relationships, groups, institutions, societies and the health sector as a whole. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new ‘AI winter’ could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.

Background and objective: The rapid expansion of artificial intelligence (AI) in healthcare has resulted in substantial advances in diagnostics, prognostics, clinical decision support systems, and patient monitoring. Despite promising performance, many AI-based systems remain insufficiently understood by clinicians and patients due to their “black-box” nature. This lack of transparency may undermine trust, hinder acceptance, and limit safe integration into routine clinical practice. Explainable artificial intelligence (XAI) has emerged as a response to these challenges by enabling human-interpretable explanations of algorithmic decisions. The objective of this narrative review is to synthesize current evidence on XAI in healthcare, with particular emphasis on its technical foundations, clinical applications, influence on trust and decision-making, and broader social and ethical implications. Scope of review: This review synthesizes literature published between 2019 and 2025 addressing explainability in medical AI. The analysis includes methodological studies, clinical evaluations, human–computer interaction research, and social science investigations related to transparency, trust, accountability, and acceptance of AI systems. Relevant publications were identified through structured searches of PubMed, MEDLINE, Scopus, and Google Scholar using keywords related to explainable AI, interpretability, ethics, trust, and clinical decision support. Findings: XAI methods—including feature attribution, model simplification, counterfactual explanations, and visualization techniques—demonstrate potential to enhance clinician understanding of AI outputs and increase confidence in algorithm-assisted decisions. Evidence suggests that explainability may support diagnostic accuracy, reduce automation bias, and facilitate error detection. However, explainability alone does not ensure trust. Clinical context, user expertise, organizational culture, and regulatory frameworks play critical roles in shaping the adoption and appropriate use of explainable systems. Empirical research addressing patient perspectives remains limited. Conclusions: Explainable AI constitutes an important step toward the responsible and socially acceptable integration of intelligent systems in healthcare. While XAI can enhance transparency and trust, its effectiveness depends on thoughtful design, contextual adaptation to clinical workflows, and alignment with user needs. Further interdisciplinary research is required to standardize explainability approaches, evaluate their real-world impact on clinical outcomes, and address the ethical, legal, and societal challenges associated with medical AI.

This article aims to examine the philosophy of Artificial Intelligence (AI) in healthcare and present a novel framework that could bridge philosophy, ethics, and leadership to promote the responsible and human-centred integration of AI. Moving beyond efficiency and innovation, it explores the deeper philosophical, moral, and human dimensions of AI's evolving role in care delivery. The proposed framework incorporates teleology, ontology, epistemology, axiology, and ethics to provide a structured foundation for guiding AI development, implementation, and governance through purpose, knowledge, values, and moral action. Grounded in these principles, it highlights the leadership approaches that foster accountability, organizational readiness, and ethical stewardship in AI adoption. These insights inform the development of a framework designed to align AI with human values and to promote compassionate, ethical, and sustainable applications that enhance healthcare outcomes while preserving the essence of human care.

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

BackgroundThe integration of artificial intelligence (AI) holds substantial potential to alter diagnostics and treatment in health care settings. However, public attitudes toward AI, including trust and risk perception, are key to its ethical and effective adoption. Despite growing interest, empirical research on the factors shaping public support for AI in health care (particularly in large-scale, representative contexts) remains limited.ObjectiveThis study aimed to investigate public attitudes toward AI in patient health care, focusing on how AI attributes (autonomy, costs, reliability, and transparency) shape perceptions of support, risk, and personalized care. In addition, it examines the moderating role of sociodemographic characteristics (gender, age, educational level, migration background, and subjective health status) in these evaluations. Our study offers novel insights into the relative importance of AI system characteristics for public attitudes and acceptance.MethodsWe conducted a factorial vignette experiment with a probability-based survey of 3030 participants from Germany’s general population. Respondents were presented with hypothetical scenarios involving AI applications in diagnosis and treatment in a hospital setting. Linear regression models assessed the relative influence of AI attributes on the dependent variables (support, risk perception, and personalized care), with additional subgroup analyses to explore heterogeneity by sociodemographic characteristics.ResultsMean values between 4.2 and 4.4 on a 1-7 scale indicate a generally neutral to slightly negative stance toward AI integration in terms of general support, risk perception, and personalized care expectations, with responses spanning the full scale from strong support to strong opposition. Among the 4 dimensions, reliability emerges as the most influential factor (percentage of explained variance [EV] of up to 10.5%). Respondents expect AI to not only prevent errors but also exceed current reliability standards while strongly disapproving of nontraceable systems (transparency is another important factor, percentage of EV of up to 4%). Costs and autonomy play a comparatively minor role (percentage of EVs of up to 1.5% and 1.3%), with preferences favoring collaborative AI systems over autonomous ones, and higher costs generally leading to rejection. Heterogeneity analysis reveals limited sociodemographic differences, with education and migration background influencing attitudes toward transparency and autonomy, and gender differences primarily affecting cost-related perceptions. Overall, attitudes do not substantially differ between AI applications in diagnosis versus treatment.ConclusionsOur study fills a critical research gap by identifying the key factors that shape public trust and acceptance of AI in health care, particularly reliability, transparency, and patient-centered approaches. Our findings provide evidence-based recommendations for policy makers, health care providers, and AI developers to enhance trust and accountability, key concerns often overlooked in system development and real-world applications. The study highlights the need for targeted policy and educational initiatives to support the responsible integration of AI in patient care.

The purpose of this research is to identify and evaluate the technical, ethical and regulatory challenges related to the use of Artificial Intelligence (AI) in healthcare. The potential applications of AI in healthcare seem limitless and vary in their nature and scope, ranging from privacy, research, informed consent, patient autonomy, accountability, health equity, fairness, AI-based diagnostic algorithms to care management through automation for specific manual activities to reduce paperwork and human error. The main challenges faced by states in regulating the use of AI in healthcare were identified, especially the legal voids and complexities for adequate regulation and better transparency. A few recommendations were made to protect health data, mitigate risks and regulate more efficiently the use of AI in healthcare through international cooperation and the adoption of harmonized standards under the World Health Organization (WHO) in line with its constitutional mandate to regulate digital and public health. European Union (EU) law can serve as a model and guidance for the WHO for a reform of the International Health Regulations (IHR).

What sets AI systems and AI-powered medical robots apart from all other forms of advanced medical technology is their ability to operate at least to some degree autonomously from the human health care practitioner and to use machine-learning to generate new, often unforeseen, analysis and predictions. This poses challenges under the current framework of laws, regulations, and ethical guidelines applicable to health care in South Africa. The article outlines these challenges and sets out guiding principles for a normative framework to regulate the use of AI in health care. The article examines three key areas for legal reform in relation to AI in health care. First, it proposes that the regulatory framework for the oversight of software as a medical device needs to be updated to develop frameworks for adequately regulating the use of such new technologies. Secondly, it argues that the present HPCSA guidelines for health care practitioners in South Africa adopt an unduly restrictive approach centred in the outmoded semantics of telemedicine. This may discourage technological innovation that could improve access to health care for all, and as such the guidelines are inconsistent with the national digital health strategy. Thirdly, it examines the common law principles of fault-based liability for medical negligence, which could prove inadequate to provide patients and users of new technologies with redress for harm where fault cannot clearly be attributed to the healthcare practitioner. It argues that consideration should be given to developing a statutory scheme for strict liability, together with mandatory insurance, and appropriate reform of product liability pertaining to technology developers and manufacturers. These legal reforms should not be undertaken without also developing a coherent, human-rights centred policy framework for the ethical use of AI, robotics, and related technologies in health care in South Africa.

This essay examines the enormous effects of machine learning and artificial intelligence (AI) on healthcare. Through data analysis, AI is transforming disease detection and prediction and improving the precision of diagnoses. By accelerating medication discovery and improving individualized treatment programs, it is revolutionizing both treatment and drug development. AI is promoting customized medicine by using genetic information to customize therapies. Through automation and optimized resource allocation, it is streamlining hospital processes. The importance of ethical considerations is significant; they center on data privacy, bias reduction, and accountability. The study highlights potential avenues for AI development, such as AI-driven drug discovery, predictive and preventative healthcare, advances in genomic medicine, enhanced medical imaging, and more robotics and automation. Predictive analytics, telehealth, AI virtual assistants, and AI in mental healthcare are all expected to grow. These developments have the potential to improve health care, streamline processes, and boost scientific inquiry. To use AI in healthcare in a fair and ethical manner, however, and usher in a future that is more patient-centric, accurate, and accessible internationally, difficulties related to data quality, ethics, regulation, and prejudice must be addressed.