Demographic biases in AI-generated simulated patient cohorts: a comparative analysis against census benchmarks

Abstract

In their default state, the evaluated models create synthetic patient pools that exclude younger, older, female and most minority-ethnic representations. Such demographically narrow outputs threaten to normalise biased clinical expectations and may undermine efforts to prepare students for equitable practice. Baseline auditing of model behaviour is therefore essential, providing a benchmark against which prompt-engineering or data-curation strategies can be evaluated before generative systems are integrated into formal curricula.

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