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Just as drugs are withdrawn when they prove unsafe, AI tools should have clear triggers for being switched off, and someone responsible for pulling the switch.
No framework exists for taking a deployed model out of service: models trained on outdated staging or treatment eras continue to run. The proposal defines decommissioning triggers (performance below threshold on monitoring, guideline change affecting the task, vendor withdrawal, unaddressed red-team findings), assigns responsibility (site clinical AI officer, vendor, regulator), and requires notification of affected patients where results may have been wrong, mirroring device recall processes.
Shares A public registry of every AI model used in cancer care, A standard for monitoring AI performance drift with pause thresholds, 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 A public registry of every AI model used in cancer care, 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 A standard for monitoring AI performance drift with pause thresholds, 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 A public registry of every AI model used in cancer care, 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.