{"entity":{"id":"idea-moon-continuous-ai-validation-registry","kind":"idea","name":"Continuous prospective validation for every oncology AI tool after deployment","aka":[],"tldr":"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.","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":[],"cancers":[],"sections":[],"technologies":["radiology-ai-screening","digital-pathology-ai","pathology-foundation-model"],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":["b-ai-validation","b-regulatory-fragmentation"],"keyPapers":["paper-wu-nat-med"],"journals":[],"dependsOn":[],"notes":[],"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.","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","cost":"medium","horizonYears":3},"route":"/ideas/idea-moon-continuous-ai-validation-registry/","neighbours":{"technology":[{"id":"radiology-ai-screening","kind":"technology","name":"AI in radiology","route":"/technologies/radiology-ai-screening/"},{"id":"digital-pathology-ai","kind":"technology","name":"Digital pathology & AI","route":"/technologies/digital-pathology-ai/"},{"id":"pathology-foundation-model","kind":"technology","name":"Pathology & radiology foundation models","route":"/technologies/pathology-foundation-model/"}],"bottleneck":[{"id":"b-ai-validation","kind":"bottleneck","name":"AI that is built but not validated or deployed","route":"/bottlenecks/b-ai-validation/"},{"id":"b-regulatory-fragmentation","kind":"bottleneck","name":"Regulatory divergence between regions","route":"/bottlenecks/b-regulatory-fragmentation/"}],"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/"}]}}