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

ABSTRACT:

Background

Generative artificial intelligence models are being introduced as low-cost tools for creating simulated patient cohorts in undergraduate medical education. Their educational value, however, depends on the extent to which the synthetic populations mirror real-world demographic diversity. We therefore assessed whether two commonly deployed large language models produce patient profiles that reflect the current age, sex, and ethnic composition of the UK.

Methods

GPT-3.5-turbo-0125 and GPT-4-mini-2024-07-18 were each prompted, without demographic steering, to generate 250 UK-based 'patients'. Age was returned directly by the model; sex and ethnicity were inferred from given and family names using a validated census-derived classifier. Observed frequencies for each dem

SUBMITTER: Veenhuizen M 

PROVIDER: S-EPMC12625206 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature