Who Shapes AI Rules in Health Care?

This analysis synthesizes 6 sources published February 23–24, 2026. Editorial analysis by the PhysEmp Editorial Team.

Why this theme matters now

The core tension is no longer whether AI will be used in health care; it is who will set the rules that determine which algorithms are safe, which products reach clinicians, and how AI-driven decisions affect coverage and careers. Recent regulatory petitions seeking narrower premarket review, federal and state moves to limit insurer automation, and renewed calls for clinician-led governance together show a contest over the architecture of oversight — a contest that will materially change clinical workflows, liability exposures, and recruiting priorities.

Those responsible for hiring and retention must monitor this closely because regulatory design directly shapes clinician workload (for example, automation of prior authorization versus algorithmic denials), institutional liability, and the combination of technical and governance skills hospitals will prize. This conversation sits squarely inside trust, risk, and governance in healthcare, and choices made now will ripple through credentialing, compensation, and workforce strategy.

1) Premarket versus post-market: where accountability lands

Recent vendor petitions seeking narrower definitions of what triggers FDA premarket review reveal a deliberate strategy: accelerate market access by reclassifying updates or limiting the scope of oversight. Regulators face a tradeoff. Easing premarket hurdles speeds availability of tools that can benefit patients but pushes more of the detection and mitigation burden downstream — onto hospitals, clinicians, and payers. That transfer changes the calculus of adoption: health systems must decide whether to accept more operational risk or to insist on stricter procurement controls.

For physicians considering a job change, the practical implication is clear. Employers that favor rapid deployment of lightly regulated tools will need clinicians to validate models locally, monitor performance in real time, and participate in incident investigations. Recruiters should therefore highlight whether an employer has structured post-market monitoring, dedicated clinical informatics capacity, and clear governance channels — not just a glossy AI strategy.

Call Out — Regulatory Tradeoff: Allowing more AI to enter the market without robust premarket review shifts oversight responsibilities to health systems and clinicians. That transfer increases hidden labor and elevates demand for AI-literate hires: clinical informaticists, validation leads, and governance officers.

2) Physician leadership: necessary, but not sufficient

There is broad agreement that clinicians should lead the design of AI governance because they provide indispensable clinical context and risk judgment. However, expecting physicians to lead without the organizational supports required for sustained governance is unrealistic. Effective clinician leadership depends on a stable administrative backbone: product managers, data engineers, legal counsel, compliance officers, and protected time for clinician leaders to do the work.

For physicians, governance roles can be powerful career differentiators — offering influence over clinical tools and workflows — but they can also exacerbate burnout if institutions assign responsibility without funding the necessary nonclinical infrastructure. Recruiters should therefore position governance openings with explicit resources: FTE support, budget lines for monitoring, and clear escalation routes to executive leadership.

3) Payers, prior authorization, and the tug-of-war over automation

Policy debates about insurer use of AI — especially around automated denials — illustrate a second axis of governance: payers as algorithmic gatekeepers. Federal and state interventions attempting to curtail automated coverage denials sit alongside CMS efforts to modernize prior authorization using regulated tools. The result will be an uneven landscape in which some payers adopt aggressive automation while others move toward standardized, regulated solutions designed to reduce clinician paperwork.

Health systems that serve diverse payer mixes will face inconsistent administrative burdens across patients. Physicians moving between organizations should evaluate the payer environment they will practice in; recruiters and executives must anticipate variable administrative loads and plan training and staffing accordingly.

4) The evaluation deficit: a practical governance gap

A recurring weakness across the coverage is the absence of standardized evaluation frameworks that allow purchasers to compare AI tools meaningfully. Controlled trials and regulatory clearances are necessary milestones but not sufficient. Real-world performance across different populations, clinical settings, and workflow integrations matters more for day-to-day safety and effectiveness. Without common metrics, interoperable reporting standards, and contractually required transparency, buyers cannot reliably detect performance drift or make apples-to-apples comparisons.

Mainstream reporting often treats regulatory clearance or approval as a finish line; that framing is incomplete. True governance requires routine, comparable post-market performance data, standardized test datasets, and vendor obligations to report model updates and outcomes — governance elements that many procurement processes currently neglect.

Call Out — Evaluation Gap: Debates that focus solely on premarket review miss a larger governance need: standardized, cross-vendor post-market metrics and reporting that let health systems and payers track safety and effectiveness over time.

Implications for health systems, clinicians, and recruiters

Short term, hospitals must choose between speed-to-adoption (and absorbing more post-market risk) and conservative procurement with stronger validation pipelines. That choice informs hiring: rapid adopters will need clinicians who can do local validation and monitor deployed models; cautious systems will value clinicians skilled in change management, vendor negotiation, and structured rollout processes.

For physicians evaluating career moves: ask concrete questions about governance maturity — Is there a multidisciplinary AI governance committee? Are incident response protocols established? Is clinician time compensated and protected? For hospital executives and recruiters: make governance capacity a clear talent differentiator. Create roles that combine clinical credibility with operational authority, fund the nonclinical teams that sustain governance, and communicate those investments in recruiting materials.

Where mainstream coverage is incomplete

Most coverage frames the issue as a tradeoff between regulation and innovation speed. That is an oversimplification. A more consequential question is distributional: who ultimately bears responsibility for safety and ongoing performance — regulators, vendors, payers, or health systems? And within systems, will clinicians be given the time, authority, and resources to carry governance responsibilities? Treating governance as only a legal or technological problem ignores the workforce and budget realities that determine whether oversight will actually work.

Conclusion

AI governance in health care is being shaped across multiple arenas: regulatory petitions, federal and state policy actions, payer behavior, and calls for clinician-led design. The meaningful choice for organizations is not simply which tools to adopt but how to build governance that assigns responsibility, funds necessary infrastructure, and recruits the right talent. Systems that clarify where accountability lies, fund nonclinical supports, and hire for governance capability will be positioned to realize AI’s benefits while protecting clinicians and patients.