The FDA's traditional premarket review system is proving insufficient for regulating AI medical devices, as many exhibit output unpredictability that only emerges after deployment. To address this, researchers propose a targeted postmarket surveillance framework combining periodic revalidation using existing test data with ongoing performance monitoring through aggregated health system registries. This approach focuses on AI devices where unpredictability intersects with meaningful patient harm risks, balancing innovation with safety while minimizing costs and avoiding regulatory capture.
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Source: paragoninstitute.org
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