USA Law and Practice Contributed by: Nadia de la Houssaye, Andy Lee, Jason Loring and Graham Ryan, Jones Walker LLP
ers, healthcare institutions and others in the AI sup - ply chain. For example, a consultation that results in patient harm might implicate the treating physician, the health system and the developers of clinical deci - sion support software used during the encounter. Other considerations include the following. • Product liability: AI-enabled medical devices may be subject to product liability claims under theories of design defect, manufacturing defect or failure to warn. The “black box” nature of some AI systems can further complicate product liability analysis. • Institutional liability: Healthcare institutions face potential liability for AI system selection, implemen - tation, training and oversight. Hospitals and health systems must establish appropriate governance frameworks, staff training programmes and quality assurance processes. • Insurance coverage considerations: The distribu - tion of liability will likely shift as device manufactur - ers, algorithm developers, administrators and other parties include AI products in deployed diagnostic and treatment tools. Since professional liability insurance policies may not cover (adequately or at all) AI-related claims, healthcare providers may be forced to secure specialised coverage or policy modifications. • Emerging liability theories: Recent litigation has introduced novel liability theories specific to AI systems, including algorithmic negligence claims when AI systems produce systematically biased outcomes, breach of fiduciary duty for inappropri - ate reliance on AI recommendations, consumer protection claims for misrepresentation of AI capa - bilities and contract claims for AI systems failing to meet performance specifications. 4.2 Patient Harm and Malpractice Traditional malpractice standards must adapt to address algorithm-based recommendations and decision support. In April 2024, the Federation of State Medical Boards released recommendations to its members indicating, among other suggestions, that they should hold clinicians liable if AI technol - ogy makes a medical error. Healthcare providers must understand AI system limitations and maintain appro -
priate clinical judgment when incorporating algorith - mic recommendations into patient care decisions. Causation Challenges When algorithms influence or drive medical decisions, determining responsibility for adverse outcomes pre - sents novel legal challenges not fully addressed in existing liability frameworks. Among other issues, courts must evaluate whether AI system recommen - dations served as a proximate cause of patient harm, as well as the impacts of the healthcare provider’s independent medical judgment and other contribut - ing factors. Documentation and Evidence Requirements Healthcare providers must maintain detailed docu - mentation of AI system use, including the specific recommendations provided, clinical reasoning for accepting or rejecting algorithmic guidance and any modifications made to AI-generated suggestions. Expert Testimony Considerations AI-related malpractice cases may require expert witnesses with specialised knowledge of medical practice and existing AI technology capabilities and limitations. Such experts should have the experience necessary to evaluate whether healthcare provid - ers used AI systems in an appropriate manner and whether algorithmic recommendations met relevant standards. Burden of Proof Considerations Plaintiffs in AI-related malpractice cases face chal - lenges proving that AI system errors directly caused patient harm, particularly when healthcare providers retained decision-making authority. Decisions regard - ing potential liability often depend on judgments made by lay-person jurors. 4.3 Risk Management Requirements To mitigate risks associated with healthcare AI, devel - opers, vendors, health systems and practitioners should: • develop and deploy institutional risk assessments – these assessments should evaluate potential clinical risks, cybersecurity vulnerabilities, privacy
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