USA Law and Practice Contributed by: Nadia de la Houssaye, Andy Lee, Jason Loring and Graham Ryan, Jones Walker LLP
10. Practical Considerations in Healthcare AI 10.1 Compliance Strategies Healthcare stakeholders should remain focused on a number of core compliance matters: • documentation and record-keeping – successful AI compliance requires meticulous documentation of AI system selection, validation, implementation and ongoing monitoring activities; • risk-based compliance – organisations should implement risk-based approaches to AI compli - ance that prioritise resources and attention based on the potential impact and risk level of different AI applications, particularly higher-risk AI systems; and • ongoing monitoring and assessment – monitoring and assessment programmes that track system performance, identify potential issues and ensure continued compliance with regulatory requirements and professional standards should be implemented and maintained. 10.2 Contracting and Liability Allocation Healthcare AI contracts must address complex tech - nical, legal and regulatory requirements and allocate risks and responsibilities appropriately. Contracts should cover system performance, data handling, regulatory compliance and liability allocation with suf - ficient detail to prevent disputes. Other key issues to consider include: • liability allocation mechanisms;
• FDA innovation pathways: The FDA’s Digital Health Center of Excellence was established to provide regulatory advice on digital health policy, cyberse - curity and AI/ML applications. The Digital Health Software Precertification Program pilots new approaches to regulate software-based medical devices through streamlined oversight for qualified developers. • Public-private partnerships: Should federal funding dwindle, the collaboration between government agencies and industry leaders with respect to the development of AI standards and best practices may be threatened. • State pilot programmes and demonstrations: Several states have established regulatory sand - boxes or innovation programmes specifically for healthcare technologies, including AI applications. It remains to be seen whether such initiatives will Given current uncertainties, it is difficult to predict the degree to which US agencies, businesses and other organisations will be allowed to participate in multi - national initiatives aimed at harmonising healthcare AI regulations. 9.4 Emerging Legal Challenges Given current political, legislative and regulatory uncertainties in the United States, it remains to be seen which legal challenges with respect to healthcare AI are likely to rise to the fore. That said, there remain a number of key issues that continue to be subject to ongoing scrutiny and debate, including questions involving: • continuous learning systems; • genAI; • AI integration with emerging technologies; • algorithmic accountability; • physician responsibility; • institutional oversight; and • manufacturer accountability for AI system perfor - mance. survive potential funding disruptions. 9.3 International Harmonisation
• indemnification provisions; • insurance requirements; and • service levels and expectations. 10.3 Insurance Considerations
Healthcare organisations should audit existing insur - ance coverage to identify potential gaps related to AI risks. Traditional policies may not adequately cov - er AI-specific risks such as algorithmic errors, data breaches or intellectual property infringement. The insurance industry has developed specialised products and pricing models to address AI-related risks, including cyber-liability insurance, technology
139 CHAMBERS.COM
Powered by FlippingBook