# Rules for retiring cancer AI when performance drops or the standard of care moves

Source: https://onco.cc/ideas/idea-data-ai-decommissioning-rules/  
OnCo record `idea-data-ai-decommissioning-rules` (Idea). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

Just as drugs are withdrawn when they prove unsafe, AI tools should have clear triggers for being switched off, and someone responsible for pulling the switch.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No framework exists for taking a deployed model out of service: models trained on outdated staging or treatment eras continue to run.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.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- 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.
- Proposed 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

## Sources

- 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): https://doi.org/10.1038/s41591-021-01312-x

## Connected records

- ideas: [A public registry of every AI model used in cancer care](https://onco.cc/ideas/idea-data-clinical-ai-model-registry/), [A standard for monitoring AI performance drift with pause thresholds](https://onco.cc/ideas/idea-data-drift-monitoring-standard/)
- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/)
- bottlenecks: [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/)
- key papers: [How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals](https://onco.cc/key-papers/paper-wu-nat-med/)

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