Uncovering bias and variability in how large language models attribute cardiovascular risk.

ABSTRACT: Large language models (LLMs) are used increasingly in medicine, but their decision-making in cardiovascular risk attribution remains underexplored. This pilot study examined how an LLM apportioned relative cardiovascular risk across different demographic and clinical domains. A structured prompt set across six domains was developed, across general cardiovascular risk, body mass index (BMI), diabetes, depression, smoking, and hyperlipidaemia, and submitted in triplicate to ChatGPT 4.0 mini. For each domain, a neutral prompt assessed the LLM's risk attribution, while paired comparative prompts examined whether including the domain changed the LLM's decision of the higher-risk demographic group. The LLM attributed higher cardiovascular risk to men than women, and to Black rather than white pa

SUBMITTER: Chan JTN 

PROVIDER: S-EPMC12722456 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature