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Like a trial registry, every AI tool used on real patients would be listed publicly with what it is for, what data it was trained on, how well it performed and which version is running where.
There is no public record of which AI models are deployed in which hospitals, for what indications, at what versions. The proposal is a mandatory registry (regulator-run or accredited) with a standard model card: intended use, training data summary (sites, years, demographics), validation results by subgroup, version history, deployment sites, and links to post-market performance reports. FDA's list of cleared AI devices is a partial precedent but lacks deployment and performance data.
Shares Rules for retiring cancer AI when performance drops or the standard of care moves, Mandatory post-market performance reporting for cancer AI, AI that is built but not validated or deployed.
Shares Mandatory post-market performance reporting for cancer AI, AI that is built but not validated or deployed.
Shares A liability framework for clinical AI: safe harbour for clinicians, liability for makers, Patients told which AI is used in their care, in plain language, Rules for retiring cancer AI when performance drops or the standard of care moves, Mandatory post-market performance reporting for cancer AI.