A new index from Mount Sinai researchers reveals that fragmented oversight is complicating the adoption of artificial intelligence in clinical settings.

Researchers at the Icahn School of Medicine at Mount Sinai have introduced the Health & AI Policy Index, a comprehensive analytical tool designed to track the fragmented regulatory environment governing artificial intelligence in clinical settings. Published on June 1, 2026, in the journal npj Digital Medicine, the study synthesizes 240 distinct policy initiatives enacted between 2016 and 2025 to provide a clearer view of how oversight is evolving across global health systems.

The analysis reveals that while the volume of AI-related policy is accelerating, the current governance landscape remains a disjointed collection of institutional guidance, technical standards, and localized regulations. This lack of a centralized framework forces health systems to navigate a complex web of compliance requirements without a standardized roadmap for safe deployment. The study highlights that AI tools are now embedded in critical areas ranging from patient diagnostics to administrative workflows, yet the governing structures have not kept pace with this rapid integration.

Will Moss, a health care AI policy intern at the Icahn School of Medicine at Mount Sinai and lead author of the study, notes that the rapid pace of adoption often outstrips the ability of organizations to establish comprehensive governance. The research suggests that the current environment is characterized by a reliance on disparate policy initiatives rather than a cohesive, cross-jurisdictional approach. This fragmentation introduces operational risks for health systems that must manage both the technical implementation of AI and the evolving legal expectations surrounding patient safety and data transparency.

Girish N. Nadkarni, MD, MPH, the senior author of the study and Chief AI Officer of the Mount Sinai Health System, emphasizes that successful AI adoption requires more than just technical deployment. According to Dr. Nadkarni, the long-term viability of these technologies depends on internal governance structures and clear accountability protocols that address the specific challenges of clinical environments. The study underscores that academic medical centers are increasingly assuming a leadership role in defining these real-world governance practices as they bridge the gap between innovation and regulatory compliance.

The Health & AI Policy Index serves as a diagnostic tool for policymakers and health system administrators to identify where policy efforts are concentrated and where critical gaps persist. By cataloging nearly a decade of policy activity, the researchers aim to provide a foundation for better coordination among stakeholders. The findings suggest that without a more unified approach to oversight, health systems will continue to face significant friction in scaling AI solutions responsibly.

The absence of a singular, unified framework is not merely an administrative inconvenience but a fundamental challenge to the scalability of clinical AI. As health systems attempt to integrate machine learning into patient care, they are forced to reconcile conflicting standards from various regulators and international bodies. This environment creates a high barrier to entry for smaller institutions that may lack the resources to monitor and comply with such a diverse array of evolving guidelines.

The significance of this research lies in its ability to quantify the current state of AI governance. The research shifts the focus from theoretical risks to actionable policy analysis. By mapping the regulatory landscape, the study provides a necessary baseline for understanding how accountability is being defined. It highlights the tension between the speed of technological innovation and the cautious, often slow, nature of regulatory development.

Future research will likely focus on how these governance approaches vary across different geographic jurisdictions and how health systems translate high-level policy into daily clinical operations. The authors intend to expand the index to track how institutional frameworks adapt as AI technologies become more autonomous and integrated into high-stakes medical decision-making. These developments will be critical for stakeholders as they attempt to balance the benefits of AI-driven efficiency with the imperative of patient safety and ethical oversight.

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