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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.
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.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, Pathology & radiology foundation models, AI that is built but not validated or deployed, Digital pathology & AI.
Shares Pathology & radiology foundation models, AI that is built but not validated or deployed, AI in radiology, Digital pathology & AI.
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, Regulatory divergence between regions.
Shares Pathology & radiology foundation models, AI that is built but not validated or deployed, Digital pathology & AI.
Shares AI that is built but not validated or deployed, Regulatory divergence between regions.
Shares AI that is built but not validated or deployed, Regulatory divergence between regions.
Shares AI that is built but not validated or deployed, AI in radiology, Digital pathology & AI.