# Continuous prospective validation for every oncology AI tool after deployment

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

## TL;DR

Cancer AI tools are approved on old test data and then never checked again. Require every deployed tool to report its real-world performance continuously, in public.

## Summary

Radiology, pathology and prognostic AI tools in oncology are cleared on retrospective datasets; performance drifts with scanners, populations and practice, and post-market surveillance is minimal. The proposal is a regulatory requirement and shared infrastructure: every deployed oncology AI tool feeds outcome-linked performance metrics to a registry, stratified by site and demographic group, with public dashboards, drift alerts and pre-agreed thresholds for suspension, harmonised across regulators.

## Fields

- Kind: Idea
- Last checked: 2026-09-08
- Hypothesis: Continuous validation detects clinically significant performance degradation in a meaningful share of deployed tools within two years and raises clinician trust and adoption of tools that perform well.
- Rationale: Pharmacovigilance is standard for drugs; algorithms change performance more readily and silently, and a shared registry spreads the cost.
- Proposed test: Pilot registry across three tool categories in ten hospitals; measure detection of drift, time to corrective action, and comparison of registry performance with cleared claims.
- Maturity: early-clinical
- Actor: regulator

## 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

- technologies: [AI in radiology](https://onco.cc/technologies/radiology-ai-screening/), [Digital pathology & AI](https://onco.cc/technologies/digital-pathology-ai/), [Pathology & radiology foundation models](https://onco.cc/technologies/pathology-foundation-model/)
- bottlenecks: [AI that is built but not validated or deployed](https://onco.cc/bottlenecks/b-ai-validation/), [Regulatory divergence between regions](https://onco.cc/bottlenecks/b-regulatory-fragmentation/)
- 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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