Healthcare Leaders Demand Federal AI Rules to End Regulatory Chaos

WASHINGTON, DC – January 15, 2026 – A coalition of top healthcare industry leaders is urgently calling on the federal government to establish a unified national framework for artificial intelligence, warning that a chaotic and fragmented landscape of state-level rules is stifling life-saving innovation and creating significant risks for patients.

The call to action comes via a new report, “Unleashing AI’s Potential for Patients: A Cross-Sectoral Roadmap for Healthcare,” released by the Healthcare Leadership Council (HLC) and the global consulting firm ZS. The document argues that without a clear, consistent federal approach, the full potential of AI to improve diagnostics, streamline care, and lower costs will remain locked away behind a wall of regulatory uncertainty.

“AI can further revolutionize patient care and reduce provider burden, but only if policymakers and industry move in lockstep,” said Maria Ghazal, President and 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.”

A Growing Regulatory Maze

The report’s central warning focuses on the dangers of an unevenly developing policy environment. As federal agencies work at their own pace, states have rushed to fill the void, creating a complex and often contradictory set of rules that vary dramatically from one jurisdiction to another.

This legislative patchwork is already a reality. In 2025 alone, over 250 AI-related bills were introduced in 47 states, with 33 becoming law. These laws cover everything from patient notification requirements to practitioner monitoring and the definition of professional practice. For instance, Oregon has moved to prohibit “nonhuman entities” from using nursing titles, while Montana’s “Right to Compute” law imposes risk management requirements on AI used in critical infrastructure, including healthcare.

A prime example of this state-led experimentation is Utah’s first-in-the-nation pilot program, launched in January 2026. The program allows an AI system to autonomously renew prescriptions for nearly 200 common, non-controlled medications. While the initiative, which operates within a state-sanctioned “regulatory sandbox,” is designed to address workforce shortages and improve access, it also exemplifies the kind of divergence that industry leaders fear. The HLC and ZS report argues that such state-by-state approaches could lead to a system where the quality and safety of AI-driven healthcare depend on a patient's zip code.

A Blueprint for Smarter, Unified Healthcare

Developed from in-depth interviews with experts from 27 HLC member organizations—spanning hospitals, insurers, pharmaceutical companies, and tech firms—the report offers more than just a diagnosis of the problem. It provides a detailed blueprint for public-private collaboration, identifying three core barriers to AI adoption and proposing specific policy solutions.

Governance and Regulatory Complexity: The primary barrier is the lack of a coherent legal framework. The report recommends establishing centralized federal legislation to create a predictable environment, modernizing existing regulations to be AI-ready, and clarifying accountability and liability protections for the responsible use of AI tools in patient care.

Data Access and Infrastructure Challenges: AI is only as good as the data it’s trained on. The report calls for policies that fortify data readiness and sharing, establish clear transparency standards for algorithms, actively mitigate data bias to ensure equity, and develop universal definitions for AI applications in healthcare.

Capabilities and End-User Trust: Technology is useless if clinicians are not trained to use it and patients do not trust it. The third barrier focuses on the human element, recommending enhanced workforce training, incentives for employee education on AI systems, and a revamping of medical school curricula to make future clinicians inherently AI-ready.

“This report serves as a practical guide for how public and private stakeholders can work together to unlock AI’s full potential in healthcare,” stated Bill Coyle, Chairperson at ZS. “Drawing on insights from leaders actively implementing these technologies, it highlights real-world barriers to adoption and the policy solutions needed to overcome them.”

Federal Agencies Grapple with Oversight

The call for a unified framework lands in a Washington D.C. already active, yet uncoordinated, on AI policy. The Food and Drug Administration (FDA) has authorized over 950 medical devices that use AI or machine learning, employing a “total product life cycle” approach to regulation. The Department of Health and Human Services (HHS) has its own AI Strategic Plan, and the Centers for Medicare & Medicaid Services (CMS) has already ruled that AI algorithms alone cannot be used to determine medical necessity for Medicare Advantage patients.

However, these efforts remain siloed. This complexity is further compounded by shifts in executive branch priorities, including a December 2025 executive order from the Trump Administration aimed at deregulating the AI landscape to spur innovation, a move that could potentially challenge existing state laws and intensify calls for a single national standard.

Balancing Breakthroughs with Public Trust

Beyond the regulatory mechanics, the report and the broader industry conversation emphasize the critical need to build and maintain patient trust. Stakeholder groups like the American Hospital Association (AHA) have echoed the call for “smarter AI regulation” that is flexible and sector-specific, aligning with existing frameworks like HIPAA rather than creating new layers of bureaucracy.

Similarly, AdvaMed, which represents medical technology companies, has pushed for clear regulatory pathways and, crucially, updated reimbursement models from CMS to ensure that innovative AI-based services are financially viable and accessible to patients. Their advocacy highlights that without a clear payment pathway, even the most groundbreaking and well-regulated AI tools may never reach the bedside.

The consensus among healthcare leaders is clear: AI holds immense promise, but its journey from algorithm to patient bedside is fraught with challenges. The HLC and ZS report frames this as a pivotal moment, urging a collaborative effort to build the guardrails that will allow innovation to flourish safely and equitably across the entire healthcare system.