Cambridge Lab's ML Research Shows How the "Average Patient" Fails Women's Health
MihaelaVDS · x · 2026-08-27
The van der Schaar Lab published a long-form piece, When the Average Patient Fails Women, reviewing years of its machine learning research across cardiovascular disease, breast cancer screening, and clinical pathways.
The core argument: women may be present in a dataset yet still poorly served by the model learned from it. A single risk score, diagnostic threshold, or treatment pathway can perform well across a population while overlooking how age changes risk and how different information matters for different patients. Closing the historical data gap is necessary but not sufficient—more data alone can just produce a more confident average.
The lab's work shows carefully designed ML can do more: uncover clinically important differences without pre-specifying subgroups, personalise screening and treatment while representing uncertainty, and make the human decisions behind clinical pathways open to inspection. The goal is not an "average woman" but better ML, better science, and better healthcare for everyone.
Related event: Cambridge Lab: Medicine Built on the 'Average Patient' Fails Women(2 posts)→
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