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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.
AI can take over one reader's work in double-reading screening programmes while finding more cancers. Whether the extra cancers found are ones that would have harmed women, and whether interval cancers fall, is the question the trial's primary endpoint will answer.
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