OnCo
ideasIdea

Require stage-shift or interval-cancer endpoints for AI in cancer screening

AI for screening should be judged on whether it finds dangerous cancers earlier and misses fewer, not just on whether it agrees with radiologists on old images.

AI in mammography, lung CT and colonoscopy is evaluated on retrospective detection metrics that reward finding more lesions regardless of clinical significance, which risks overdiagnosis. The proposal requires, for adoption in organised screening programmes, evidence on interval cancer rates, stage distribution of detected cancers and recall rates from prospective studies (randomised or well-designed stepped implementations), with post-implementation monitoring of the same endpoints via registry linkage.

Hypothesis
Judged on interval cancers and stage shift, some AI tools with strong retrospective performance will show no benefit or increased overdiagnosis, while others will reduce interval cancers, and the endpoint requirement will steer development toward the latter.
Rationale
Screening's history (PSA, thyroid ultrasound) shows that detecting more is not the same as helping; MASAI and similar trials show the correct endpoints are measurable within a programme.
What would test it
Adopt the endpoint requirement in one national screening programme; evaluate two AI tools via stepped implementation with registry-linked interval cancer follow-up over three years.
Maturity
early clinical
Who has to act
regulator
Cost to try
Medium ($1M to $50M)
Years to first evidence
4
Bottlenecks it attacks

Key papers

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