Artificial intelligence in medicine: An ethical and legal review.

ETHICS AND LEGAL LIABILITY IN ARTIFICIAL INTELLIGENCE APPLIED TO MEDICINE

It is concluded that the responsible use of artificial intelligence in medicine depends on human supervision, professional training, and the integration of bioethical principles, ensuring technological innovation aligned with safety and patient-centered care.

Medicolegal aspects of the use of artificial intelligence in healthcare: challenges, current regulations, and future directions

Artificial Intelligence in Clinical Decision-Making: A Systematic Review

Background: Artificial Intelligence (AI) has emerged as one of the most transformative technologies in modern healthcare, significantly influencing clinical decision-making processes. AI-driven systems, including machine learning algorithms, deep learning models, natural language processing, and clinical decision support systems, have demonstrated the ability to analyze vast amounts of healthcare data, identify patterns, predict outcomes, and support evidence-based clinical decisions. As healthcare systems face increasing patient complexity, workforce shortages, and demands for improved quality of care, AI offers opportunities to enhance diagnostic accuracy, treatment planning, risk prediction, and healthcare efficiency. However, concerns regarding algorithm transparency, ethical accountability, data privacy, bias, and professional acceptance continue to challenge its widespread adoption. Objectives To systematically review the evidence regarding the role of artificial intelligence in clinical decision-making across healthcare settings. To identify the benefits and effectiveness of AI-supported clinical decision-making systems in improving patient outcomes. To examine challenges, ethical considerations, and barriers associated with AI implementation in clinical decision support. To evaluate implications for healthcare professionals, particularly nurses and physicians, in AI-assisted clinical practice. Methods: This systematic review was conducted according to Joanna Briggs Institute (JBI) methodology and reported following PRISMA 2020 guidelines. Electronic databases including PubMed, Scopus, Web of Science, CINAHL, ScienceDirect, and Google Scholar were searched for peer-reviewed studies published between 2019 and 2025. The review question was structured using the PICOT framework. Methodological quality was assessed using appropriate JBI Critical Appraisal Tools. Due to heterogeneity among study designs and outcomes, findings were synthesized narratively. Results: Thirty-five studies met the inclusion criteria. Evidence demonstrated that AI-assisted clinical decision-making significantly improved diagnostic accuracy, risk stratification, treatment planning, predictive analytics, medication management, and workflow efficiency. AI systems were particularly effective in radiology, oncology, cardiology, intensive care, and chronic disease management. However, challenges related to explainability, algorithmic bias, legal liability, data privacy, workforce adaptation, and ethical governance were consistently reported. Conclusion: Artificial intelligence has substantial potential to strengthen clinical decision-making and improve healthcare outcomes. Successful integration requires robust regulatory frameworks, transparent algorithms, ethical governance, clinician training, and preservation of patient-centered care principles. AI should function as a supportive tool that enhances rather than replaces human clinical judgment.    

Liability Framework to Regulate Artificial Intelligence in Healthcare

Analysis of six areas of artificial intelligence in healthcare concludes that a workable framework must combine the principles of medical negligence law with data protection, consumer protection and sectoral safety requirements, together with governance mechanisms for artificial intelligence such as human oversight, audit logs, explainability and grievance redressal.

Legal and Ethical Considerations of Artificial Intelligence in Medical Imaging in India

Background Artificial intelligence is being deployed in radiology faster than the rules and regulations required to govern it. In India, this is further complicated by disparities in access to healthcare and a data protection legal framework that is in a transition phase until it becomes fully operational on May 13, 2027. Understanding these legalities is no longer optional for doctors who are in practice, using AI in the clinic, or participating in the development of AI. Methods This is a narrative review drawing on existing articles, Indian laws, and current national governance frameworks related to the use of AI in medical imaging in India. Results This review discusses the ethical and legal considerations that come with using AI today. We discuss algorithmic bias, the economic divide that limits AI access, the lack of transparency and explainability of black-box models, the threat to radiology residents from automation bias, and post-deployment model drift. On the legal side, we discuss the requirements of the Digital Personal Data Protection Act, 2023, regarding informed consent and hospitals as data fiduciaries, the classification of AI tools as Software as a Medical Device (SaMD), and Indian governance frameworks, including the Indian Council of Medical Research Ethical Guidelines for AI in Biomedical Research and Healthcare, 2023, and the Strategy for Artificial Intelligence in Healthcare for India (SAHI), 2026. Conclusion The review concludes with a practical good-practice checklist that gives radiologists an illustrative set of questions to help retain the critical mindset required at each stage of AI adoption. As AI use cases vary by context, specific guidance for your own use case is recommended.

Care beyond calculation: Artificial intelligence and the moral meaning of care in healthcare

Artificial intelligence (AI) is progressively embedded in healthcare practices, transforming diagnosis, clinical decision‑making, patient monitoring, and institutional resource allocation. Ethical analyses of AI in healthcare have largely focused on technical issues such as safety, bias, transparency, and accountability [1,2]. While indispensable, these analyses often presuppose a reductive view of healthcare as a technical enterprise focused primarily on efficiency and measurable outcomes. This article advances a purely theoretical ethical analysis centered on care as a moral, relational, and interpretive practice. Drawing on the ethics of care, personalist ethics, virtue ethics, and the philosophy of technology, it argues that care cannot be reduced to calculation or optimization. The article critically examines whether AI can meaningfully participate in care, identifies ethical tensions between automation and moral attentiveness, and proposes normative limits for AI use in healthcare grounded in human dignity, relational responsibility, and moral agency.