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<p>Artificial Intelligence (AI) has revolutionised healthcare by enhancing diagnostic precision, enabling personalised treatment planning, and improving patient outcomes across multiple medical disciplines. From interpreting complex medical imaging to predicting treatment responses and supporting clinical decision-making, AI systems have demonstrated remarkable capabilities. However, the integration of AI into medical practice introduces significant medico-legal and ethical challenges that require urgent attention. Key concerns include establishing liability frameworks when AI contributes to medical errors, ensuring adequate informed consent for AI-assisted care, protecting patient data privacy and addressing algorithmic bias that may perpetuate healthcare disparities. The <i>black-box</i> nature of many AI models limits explainability, hindering clinicians’ ability to justify treatment recommendations to patients and making it harder for regulators to verify safety, efficacy, and bias mitigation. Additionally, varying global regulatory frameworks create inconsistent standards for AI validation. Current legal structures, designed for traditional medical practice, struggle to accommodate AI’s unique characteristics, leaving clinicians and institutions exposed to liability risks. Successfully integrating AI into healthcare requires collaborative efforts among clinicians, developers, policymakers, and patients to establish comprehensive regulatory frameworks, standardised clinical guidelines, and robust professional training programs. Only through addressing these medico-legal challenges can AI become a reliable partner in advancing patient care, safety, and accessibility while maintaining ethical standards. This narrative review synthesises the current literature, regulatory frameworks, and ethical guidelines about the medicolegal dimensions of AI in healthcare, without adherence to a formal systematic review protocol.</p>

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

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Madunil Anuk Niriella,
  • Krishanni Prabagar,
  • Indeewari Prathibha Wijesingha,
  • Buddhi Jayathilleke,
  • Thanuki Natasha Goonasinghe,
  • Munaweera Thanthreege Dilum Lakshan,
  • Arjuna Priyadarshin De Silva,
  • Indira Kithulwatta

摘要

Artificial Intelligence (AI) has revolutionised healthcare by enhancing diagnostic precision, enabling personalised treatment planning, and improving patient outcomes across multiple medical disciplines. From interpreting complex medical imaging to predicting treatment responses and supporting clinical decision-making, AI systems have demonstrated remarkable capabilities. However, the integration of AI into medical practice introduces significant medico-legal and ethical challenges that require urgent attention. Key concerns include establishing liability frameworks when AI contributes to medical errors, ensuring adequate informed consent for AI-assisted care, protecting patient data privacy and addressing algorithmic bias that may perpetuate healthcare disparities. The black-box nature of many AI models limits explainability, hindering clinicians’ ability to justify treatment recommendations to patients and making it harder for regulators to verify safety, efficacy, and bias mitigation. Additionally, varying global regulatory frameworks create inconsistent standards for AI validation. Current legal structures, designed for traditional medical practice, struggle to accommodate AI’s unique characteristics, leaving clinicians and institutions exposed to liability risks. Successfully integrating AI into healthcare requires collaborative efforts among clinicians, developers, policymakers, and patients to establish comprehensive regulatory frameworks, standardised clinical guidelines, and robust professional training programs. Only through addressing these medico-legal challenges can AI become a reliable partner in advancing patient care, safety, and accessibility while maintaining ethical standards. This narrative review synthesises the current literature, regulatory frameworks, and ethical guidelines about the medicolegal dimensions of AI in healthcare, without adherence to a formal systematic review protocol.