Healthcare Sector Calls for Unified Federal Framework to Unlock AI’s Full Potential for Patients

Experts from nearly 30 organizations outline policies to spur innovation, call for “clear, forward-looking national standard that harmonizes regulations”

WASHINGTON, January 15, 2026–(BUSINESS WIRE)–A new report from the Healthcare Leadership Council (HLC) and management consulting and technology firm ZS reveals the risks of today’s highly fragmented and inconsistent approach to regulating AI in the healthcare industry and provides a roadmap of nine practical policy recommendations to address these barriers.

Despite growing momentum and interest in its potential, adoption of AI across the healthcare ecosystem is inhibited by evolving, disparate federal and state policy frameworks, regulatory uncertainty, infrastructure development needs, challenges in data interoperability, and growing concerns around fairness and trust. These barriers continue to stunt progress and impede the sector’s potential to improve patient outcomes and lower costs.

“AI can further revolutionize patient care and reduce provider burden, but only if policymakers and industry move in lockstep,” said Maria Ghazal, President & CEO of HLC. “We need a clear, forward-looking national standard that harmonizes regulations and builds trust across all constituency groups, especially patients. As this report outlines, predictability and collaboration between public and private sectors are essential to harness AI’s full potential for better outcomes and lower costs.”

The report, entitled “Unleashing AI’s Potential for Patients: A Cross-Sectoral Roadmap for Healthcare,” was developed following in-depth interviews with experts representing 27 HLC member organizations across the healthcare ecosystem. It aims to guide public-private collaboration by identifying three primary barriers to AI adoption and provides specific recommendations for addressing them:

• Barrier: Governance and Regulatory Complexity

  • Recommendation: Establish centralized legislation, modernize regulations, clarify accountability across all stakeholders, and ensure liability protections for responsible AI use in patient care.

• Barrier: Data Access and Infrastructure Challenges

  • Recommendation: Fortify data readiness and sharing, establish transparency standards, mitigate data bias, and develop universal definitions for AI use.

• Barrier: Capabilities and End User Trust

In addition, the report offers 25 actionable tactics prioritized by impact and effort. Each is illustrative and non-prescriptive, instead highlighting potential approaches to achieve the nine policy recommendations.