---



description: Wachter argues AI should be regulated more like physician licensing with competency exams and ongoing certification rather than the FDA approval model designed for drugs and devices that remain static forever
type: claim
domain: health
created: 2026-02-18
source: "DJ Patil interviewing Bob Wachter, Commonwealth Club, February 9 2026; Wachter 'A Giant Leap' (2026)"
confidence: likely
related:
- CMS is creating AI specific reimbursement codes which will formalize a two speed adoption system where proven AI applications get payment parity while experimental ones remain in cash pay limbo
- FDA is replacing animal testing with AI models and organ on chip as the default preclinical pathway which will compress drug development timelines and reduce the 90 percent clinical failure rate
- consumer willingness to pay out of pocket for AI enhanced care is outpacing reimbursement creating a cash pay adoption pathway that bypasses traditional payer gatekeeping
reweave_edges:
- CMS is creating AI specific reimbursement codes which will formalize a two speed adoption system where proven AI applications get payment parity while experimental ones remain in cash pay limbo|related|2026-03-28
- FDA is replacing animal testing with AI models and organ on chip as the default preclinical pathway which will compress drug development timelines and reduce the 90 percent clinical failure rate|related|2026-03-28
- consumer willingness to pay out of pocket for AI enhanced care is outpacing reimbursement creating a cash pay adoption pathway that bypasses traditional payer gatekeeping|related|2026-03-28
---

# healthcare AI regulation needs blank-sheet redesign because the FDA drug-and-device model built for static products cannot govern continuously learning software

Bob Wachter argues that the current regulatory framework for healthcare AI is a "square peg and round hole problem." The FDA model was built for drugs that remain chemically identical forever and devices with fixed specifications. AI systems that learn, update, and adapt continuously break every assumption in this model.

The alternative Wachter proposes: regulate AI more like physicians. Physicians pass licensing exams to practice, maintain board certification through ongoing competency testing, and face consequences when they harm patients. An analogous AI regulatory framework might require passing standardized clinical competency tests before deployment, periodic re-certification as models update, and clear accountability when AI-enabled care causes harm.

This matters because the regulatory gap is widening. AI tools are being deployed in clinical settings faster than regulators can evaluate them. The risk of overregulation -- stifling beneficial AI adoption while the healthcare system desperately needs help -- outweighs the risk of underregulation in Wachter's assessment. But "free rein" is not sustainable either. A high-level task force starting from a blank piece of paper, explicitly not constrained by existing FDA categories, is what Wachter recommends.

The AI payment problem compounds the regulatory gap. No payer currently reimburses AI-enabled mammograms despite evidence that AI mammography detects early cancers more reliably than human radiologists alone. Patients pay $50-75 out of pocket for the AI overlay. This misalignment may force the transition to value-based care, where health systems are paid a fixed amount with the expectation they will buy and use AI tools that help deliver better care at lower cost. The payment question and the regulatory question are intertwined: without a regulatory framework, payers have no basis for coverage decisions.

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### Additional Evidence (extend)
*Source: [[2026-03-20-iatrox-openevidence-uk-dtac-nice-esf-governance-review]] | Added: 2026-03-24*

UK NHS governance provides a contrasting model: DTAC (Digital Technology Assessment Criteria) + MHRA Class 1 registration + NICE Evidence Standards Framework creates a multi-layer assessment specifically for digital health tools. NHS England launched a supplier registry in January 2026 with 19 registered ambient voice transcription suppliers, all DTAC-compliant. This demonstrates an alternative regulatory approach to AI clinical tools that is more comprehensive than FDA's device-focused model.


Relevant Notes:
- [[the FDA now separates wellness devices from medical devices based on claims not sensor technology enabling health insights without full medical device classification]] -- the FDA has already created flexibility for wellness devices; clinical AI needs a parallel regulatory innovation
- [[value-based care transitions stall at the payment boundary because 60 percent of payments touch value metrics but only 14 percent bear full risk]] -- AI payment gaps may accelerate VBC adoption by making fee-for-service untenable for AI-enabled care
- adaptive governance outperforms rigid alignment blueprints because superintelligence development has too many unknowns for fixed plans -- the same principle applies to clinical AI: governance frameworks must adapt with the technology
- [[technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap]] -- healthcare AI regulation is a specific instance of this general coordination gap

Topics:
- health and wellness
