Healthcare AI Raises Financial Compliance Risks

According to PYMNTS, an analysis authored by Alaap Shah, a member of the firm and co-chair of Epstein Becker Green's AI Cross-Practice Working Group, published in TechReg Chronicle, finds that healthcare AI has moved past test-and-learn pilots into production use across clinical scheduling, drug dispensing, patient communications, and diagnostic decision-making. Shah's analysis argues the shift creates direct implications for nonclinical financial actors tied to the healthcare economy: lenders, insurers, payment companies, and FinTechs connected through payment rails, insurance products, employer health benefits, and provider lending.

The FDA is expanding oversight of AI tools that influence clinical decisions, HHS is scrutinizing how AI platforms handle patient data under existing privacy law, and the FTC has signaled interest in how AI vendors describe and market their products. Separately, California, Colorado, Utah, and other states have passed or proposed AI regulations that apply to healthcare settings, each with its own compliance obligations, leaving multi-state healthcare organizations facing a patchwork with no unifying federal standard.

Shah identifies patient-data governance as the central risk: when a third-party vendor processes patient records through an AI system, existing privacy frameworks require formal data agreements specifying permitted uses, and training a model on that data beyond the original agreement's scope can expose the institution. Interoperability compounds this, since AI is accelerating data-sharing across networks, which expands the cyberattack surface in ways financial services firms already manage in their own systems.

Vendor contracts are the enforcement flashpoint: healthcare institutions increasingly demand strong indemnification protections, audit rights over AI systems, and notification requirements when a vendor materially changes how its models operate, and FinTechs and payment companies serving this sector should expect the same pressure. Good governance, per Shah, treats AI deployment as a board-level enterprise-risk issue with legal and compliance input on every deployment decision and a live mapping of each AI tool to the regulatory frameworks that apply to it.

The content of forthcoming federal guidance on medically impactful AI, state-level rule text that references payment or consumer-finance exceptions, and how standard vendor agreements allocate responsibility when AI outputs contribute to adverse outcomes.