The adoption of artificial intelligence (AI) in healthcare is rapidly expanding, integrating itself into clinical scheduling, drug dispensing, patient communications, and diagnostic decision-making. While these advancements have marked significant progress in the industry, they also come with a set of challenges that need addressing. The healthcare sector is closely intertwined with financial institutions, which in turn become indirectly affected as regulations and liabilities in AI usage become stricter.
Alaap Shah, co-chair of Epstein Becker Green’s AI Cross-Practice Working Group, indicates that the pace at which AI tools are embraced in healthcare settings far exceeds the regulatory frameworks intended to govern them. Federal bodies, such as the Food and Drug Administration, are expanding oversight to include AI tools that assist in clinical decisions. Additionally, the Department of Health and Human Services scrutinizes AI platforms for compliance with privacy laws. Interestingly, states like California and Colorado are implementing their own AI healthcare regulations, creating a diverse regulatory environment that lacks a standardized federal governance model.
How Do Vendor Contracts Impact AI Liability?
For FinTech companies supporting healthcare clients with payment and data infrastructure, vendor contracts have become a focal point of liability concerns. These contracts dictate accountability in cases where AI system errors cause regulatory actions or patient harm claims. Healthcare entities demand indemnification protections, auditing rights, and notification clauses when significant changes occur in AI models. FinTech vendors are finding themselves similarly bound by these expectations.
Why Is Data Governance Essential?
Patient data remains at the core of healthcare AI, making data governance a central risk. Federal privacy laws protect this data, requiring healthcare providers to formalize agreements outlining the appropriate use of patient information. Training AI systems on these datasets demands heightened scrutiny, especially when data usage extends beyond initial agreements. Information sharing across networked healthcare systems increases, broadening the potential for cybersecurity threats.
Currently, healthcare systems are advised to manage AI-assisted data exchanges as distinct cybersecurity risks. Both healthcare and financial sectors converge on facing these governance challenges as interconnectivity grows, posing similar exposure concerns across industries.
“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 highlighted.
Shah emphasizes how the most effective organizations incorporate risk management into their core strategies, aligning AI deployment decisions with legal and compliance inputs. Institutions that successfully manage these frameworks can scale effectively, while overlooking compliance could result in legal and reputational harm.
The adaptability of healthcare AI continues to daunt financial executives, emphasizing the significance of maintaining good governance as AI capabilities advance. Organizations need to recognize their position in the healthcare AI value chain to navigate the complexities of modern business effectively.
“Institutions that can demonstrate to payers, regulators and business partners that their AI programs are well-managed and compliant are better positioned to scale,” Shah noted.
Healthcare AI continues to evolve, intersecting with financial services as it does so. Understanding liability, compliance, and data governance remains crucial as AI becomes more pervasive in improving healthcare operations. Those involved must remain vigilant and adaptable in a landscape ever-shifting under technological advancements.
