FDA Tells Industry to Stop Treating AI Like Static Software
A Cato Institute analysis argues that the FDA’s current software framework is poorly suited to AI systems that evolve, retrain, and behave differently across settings. The piece adds to a growing policy debate over whether regulators need a more adaptive model for software-as-medical-device oversight.
The central argument is straightforward: AI does not behave like traditional medical software, and the FDA should stop regulating it as if it does. Unlike fixed-code products, AI systems can change with new data, new prompts, and new deployment contexts, which means a one-time clearance decision may not capture their real-world risk profile.
That matters because the agency’s existing tools were built for more deterministic products. If the underlying model can drift or be updated frequently, then premarket review alone is an incomplete safeguard; the key regulatory question becomes how to monitor performance after launch and how to define acceptable boundaries for ongoing change.
The analysis lands in the middle of a larger industry shift: regulators are trying to preserve patient safety without freezing innovation. A static approval model can slow useful deployments, but an overly permissive one risks letting underperforming systems spread through clinical workflows before anyone notices the harm.
The likely answer is not deregulation so much as redesign. The FDA may need clearer rules for adaptive algorithms, real-world monitoring, and change management plans that allow approved systems to improve while still creating accountability when performance degrades.