{"entity":{"id":"idea-data-ai-decommissioning-rules","kind":"idea","name":"Rules for retiring cancer AI when performance drops or the standard of care moves","aka":[],"tldr":"Just as drugs are withdrawn when they prove unsafe, AI tools should have clear triggers for being switched off, and someone responsible for pulling the switch.","summary":"No framework exists for taking a deployed model out of service: models trained on outdated staging or treatment eras continue to run.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.","asOf":"2026-09-08","links":[{"label":"Bottleneck evidence (AI that is built but not validated or deployed): Wu et al., How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals (Nature Medicine 2021)","url":"https://doi.org/10.1038/s41591-021-01312-x"}],"tags":[],"related":["idea-data-drift-monitoring-standard","idea-data-clinical-ai-model-registry"],"cancers":[],"sections":["ai-computation"],"technologies":[],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-ai-validation"],"keyPapers":["paper-wu-nat-med"],"journals":[],"dependsOn":[],"notes":[],"hypothesis":"Explicit decommissioning rules will lead to retirement of a measurable share of currently deployed cancer AI tools that are obsolete or under-performing, and will shorten the time between trigger and action.","rationale":"Software in other safety-critical domains has defined end-of-life processes; healthcare AI has accumulated a decade of deployments with no retirement mechanism.","test":"Apply the rules to the AI inventory of one health system; count tools meeting decommissioning triggers; measure time to action.","maturity":"speculative","actor":"clinic","cost":"small","horizonYears":1},"route":"/ideas/idea-data-ai-decommissioning-rules/","neighbours":{"idea":[{"id":"idea-data-clinical-ai-model-registry","kind":"idea","name":"A public registry of every AI model used in cancer care","route":"/ideas/idea-data-clinical-ai-model-registry/"},{"id":"idea-data-drift-monitoring-standard","kind":"idea","name":"A standard for monitoring AI performance drift with pause thresholds","route":"/ideas/idea-data-drift-monitoring-standard/"}],"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"}],"bottleneck":[{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"}],"paper":[{"id":"paper-wu-nat-med","kind":"paper","name":"How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals","route":"/key-papers/paper-wu-nat-med/"}]}}