Healthcare AI Has Left the Lab. Now Comes the Hard Part.

Artificial intelligence in healthcare has evolved past a simple test-and-learn exercise.

AI tools are now embedded into clinical scheduling, drug dispensing, patient communications and diagnostic decision-making across the industry, according to an analysis authored by Alaap Shah, a member of the firm and co-chair of Epstein Becker Green’s AI Cross-Practice Working Group.

Shah’s analysis, published in TechReg Chronicle, said the shift carries direct implications beyond healthcare. Financial institutions tied to the healthcare economy through payment rails, insurance products, employer health benefits, lending to providers and health-focused consumer financial tools will feel the effects as the regulatory and liability environment tightens.

A Compliance Framework That Has Not Caught Up

The pace of AI adoption in healthcare has outrun the rules designed to govern it, Shah said. Federal agencies are working from frameworks built for a different era. The Food and Drug Administration is expanding its oversight of AI tools that influence clinical decisions. The Department of Health and Human Services, meanwhile, is scrutinizing how AI platforms handle patient data under existing privacy law, and the Federal Trade Commission has signaled interest in how AI vendors describe and market their products.

At the same time, states are moving independently, Shah said. California, Colorado, Utah and others have passed or proposed AI regulations that apply to healthcare settings, with each creating its own compliance obligations. Healthcare organizations operating across multiple states now face a patchwork of rules with no clear federal standard to anchor them.

For FinTechs providing payment or data infrastructure to healthcare clients, Shah’s analysis makes clear why this matters. Vendor contracts are the enforcement flashpoint. When an AI tool generates an error that leads to a regulatory action or a patient harm claim, the question of who absorbs that liability lands in the contract language.

Healthcare institutions demand strong indemnification protections, audit rights over AI systems, and notification requirements when a vendor makes significant changes to how its models operate, Shah said. FinTechs and payment companies supplying this sector will face the same pressure.

Data Is the Core Risk

Healthcare AI runs on patient data, and Shah identified data governance as the central risk. That data is protected under federal privacy law, and the compliance obligations attached to it are not simple.

When a third-party vendor processes patient records through an AI system, existing privacy frameworks require formal data agreements that specify how that information can and cannot be used. Training an AI model on patient data requires additional scrutiny, Shah said. If that data is used to improve a vendor’s product beyond the scope of the original agreement, institutions may be exposed.

Interoperability is a compounding factor, Shah said. AI is accelerating the ability of healthcare systems to share data across networks, and as that data moves more freely, the attack surface for cyber threats expands. Health systems are being advised to treat AI-enabled data exchange as a distinct cybersecurity risk category, and financial services firms already manage similar exposure. In short, the two sectors are converging on the same governance challenge.

What Good Governance Looks Like

The organizations navigating this period most effectively are treating it as an enterprise risk issue that sits at the board level, Shah said.

That means building internal structures that include legal and compliance input into every AI deployment decision, mapping each AI tool to the regulatory frameworks that might apply to it, and updating those assessments when the tool changes. Contracts need to include explicit accountability for what the AI does, how it changes over time, and who bears responsibility when something goes wrong.

Institutions that can demonstrate to payers, regulators and business partners that their AI programs are well-managed and compliant are better positioned to scale. Those that treat compliance as an afterthought face enforcement exposure, litigation and reputational risk that will compound as regulators grow more assertive.

The Takeaway for Financial Executives

Shah’s broader argument translated directly to the financial sector. AI adoption in healthcare is moving fast, and the governance infrastructure is still being built in real time.

The vendor contracts that organizations sign with health system clients, the data flows they facilitate, the payment products tied to healthcare spending, and any AI tools deployed in health-adjacent services all carry regulatory surface area that is expanding. Shah’s analysis makes the case that understanding where one sits in the healthcare AI value chain is critical to managing a modern financial services business.

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