USA Law and Practice Contributed by: Nadia de la Houssaye, Andy Lee, Jason Loring and Graham Ryan, Jones Walker LLP
threats and liability exposures associated with AI applications; • establish AI governance frameworks – robust AI governance frameworks address system selec - tion, validation, implementation, monitoring and updates, and are led by multidisciplinary commit - tees with the clinical, technical, legal and ethical expertise needed to oversee AI deployment; • reinforce staff training and competency – compre - hensive training and support programmes should be provided to ensure that healthcare professionals understand AI system capabilities, limitations and appropriate use cases, and that staff possess the appropriate competencies to understand escala - tion protocols when AI recommendations conflict with clinical judgment. • implement quality assurance programmes – ongo - ing quality assurance programmes monitor AI system performance, detect potential issues and ensure continued safety and effectiveness in real- world deployment; • obtain effective insurance – healthcare organisa - tions should evaluate the adequacy of existing insurance coverage for AI-related risks and, where gaps exist, consider specialised policies address - ing emerging liability exposures; and • create effective vendor management protocols – vendor management processes for AI system procurement should include due diligence on vendor capabilities, contractual risk allocation and ongoing vendor performance monitoring, among other concerns. 4.4 Defences and Limitations When disputes arise, healthcare providers, systems, and healthcare AI developers can look to several defence strategies: • regulatory compliance – healthcare providers may assert regulatory compliance as a defence against AI-related liability claims by demonstrating adher - ence to applicable laws, regulations, professional standards and institutional policies; • informed consent protections – proper informed consent processes that disclose AI system use, limitations and potential risks may provide liability protections;
• state-of-the-art defences – healthcare providers may argue that their use of AI systems reflects cur - rent state-of-the-art in medical practice, particu - larly when following established clinical guidelines and professional recommendations; • learned intermediary doctrine – this doctrine may shield AI developers from direct liability to patients, particularly where healthcare providers serve as intermediaries who evaluate and apply algorithmic recommendations; and • contractual risk allocation – well-drafted contracts between healthcare providers and AI vendors can allocate liability risks through indemnification clauses, limitation of liability provisions and clear scope of service descriptions. 5. Ethical and Governance Considerations for Healthcare AI 5.1 Ethical Frameworks In the United States, healthcare AI ethical frameworks emphasise core principles such as beneficence, non- maleficence, autonomy and justice. These principles guide AI development and deployment decisions while addressing unique challenges posed by algo - rithmic decision-making in healthcare settings. Some of the more commonly known (and mostly voluntary) frameworks include the following. • Federal ethical guidelines: Immediately following President Biden’s Executive Order on Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, 28 healthcare providers and payers voluntarily committed to compliance. Although the order has since been rescinded by President Trump, some organisations remain committed to the standards established in the order. • Professional society guidelines: Medical profes - sional societies, including the American Medical Association, have developed principles for AI use that emphasise physician responsibility, patient safety, ethical deployment practices, informed consent, algorithm transparency and professional liability. • Institutional ethics committees: Healthcare organi - sations increasingly establish AI ethics committees or incorporate AI considerations into existing insti -
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