Registry Maps ‘Fragmented’ Health AI Policy Landscape

Why It Matters

Fragmented AI governance creates operational risk and slows responsible adoption; a unified, health‑specific policy view enables systems to meet compliance and safeguard patient outcomes.

Key Takeaways

• HAPI aggregates U.S. and international health AI policies into one dataset.
• Study analyzed 240 AI-related health policies from 2016‑2025.
• Registry reveals fragmented governance hindering consistent AI adoption.
• Tags and impact scores help health systems prioritize compliance efforts.
• Identifies policy gaps, guiding future coordination and standards.

Pulse Analysis

The rapid infusion of artificial intelligence into clinical workflows has outpaced the development of coherent regulatory frameworks. While federal agencies such as the FDA have issued guidance on software as a medical device, individual states, professional societies, and international bodies have launched their own rules, creating a patchwork that health executives must navigate. This fragmentation raises compliance risk, slows deployment, and can lead to inconsistent patient protections. Understanding the full spectrum of policy activity is therefore a prerequisite for responsible AI integration and for maintaining trust among clinicians and patients.

The Health & AI Policy Index (HAPI) offers a systematic solution by consolidating federal, state, and international measures, as well as voluntary standards, into a searchable, metadata‑rich repository. Researchers at Mount Sinai screened 240 health‑AI policies published between 2016 and 2025, tagging each entry by theme, stakeholder, and anticipated impact. This granular classification enables health‑system leaders to filter for relevance, spot emerging trends, and benchmark their own governance structures against the broader policy environment. By turning a diffuse set of rules into actionable intelligence, HAPI reduces the administrative burden and supports more consistent compliance across multi‑site networks.

Beyond immediate operational gains, the index highlights systemic gaps—areas where neither legislation nor industry standards have yet coalesced. Policymakers can use these blind spots to prioritize new guidance on transparency, patient safety, and algorithmic accountability. For vendors, the visibility into jurisdictional requirements streamlines product road‑maps and reduces time‑to‑market. As health AI matures, a unified, health‑focused policy view will become a strategic asset, enabling institutions to anticipate regulatory shifts and align innovation with public‑health objectives. The HAPI model therefore sets a precedent for sector‑specific policy registries in other high‑risk technologies such as genomics and digital therapeutics.