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Set common rules for how hospitals check that an AI tool still works as the scanners, patients and practices around it change, and when it must be switched off.
Model performance shifts when scanners, staining protocols, populations or clinical practice change. Few deployments monitor this. The proposal is a technical standard: a per-site reference dataset re-scored monthly, input distribution monitoring, calibration and subgroup checks, pre-specified thresholds for alert and pause, and a documented recalibration or retraining pathway, integrated with the vendor's change control plan and reported to the registry.
Shares Mandatory post-market performance reporting for cancer AI, How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
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 How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.