Sequestered, prospectively collected benchmark datasets that no one can train on

Public benchmarks leak into training sets and go stale; retrospective validation flatters models. The proposal is a set of sequestered evaluation datasets for key cancer AI tasks (mammography, lung nodules, prostate biopsy, HER2 scoring, ctDNA calls), collected prospectively from multiple sites and countries, held by a neutral body, with evaluation only via submission of the model or an API, and results published. NIST's face recognition testing and the MICCAI challenge model are precedents.