SOURCE-LINKED INTELLIGENCE

Rethinking How We Evaluate Methodological Progress in Health AI

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work better, and under which conditions. However, such progress is thought to be hindered by difficulties in reproducibility and in defining clinically meaningful evaluation tasks. We empirically study these barriers by re-implementing 12 historical and recent algorithms within a shared evaluation framework and evaluating them on two clinical datasets, MIMIC-IV and NWICU. We compare two complementary task families: expert-authored clinically meaningf

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