Article

Evaluation of Gender Bias in the Evaluation of Synthetic Cardiovascular Disease Cases with Open Source LLMs

2025-08-19

Abstract excerpt

<h4>Objective</h4> To systematically evaluate gender bias in open-source large language models (LLMs) for cardiovascular diagnostic decision-making using controlled synthetic case vignettes. <h4>Methods</h4> We generated 500 synthetic cardiovascular cases with randomly assigned gender (male/female, equal distribution) and age (45-80 years), keeping all other clinical variables identical. Two structured prompts s...

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Literature Corpus work: 9a0e8d4a-3e7f-5aed-8c89-e3c6b2c3aaa3
DOI: 10.1101/2025.08.15.25333803

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