Large language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome.

ABSTRACT: Effective risk stratification is crucial for managing acute coronary syndrome (ACS). This study evaluated whether general-purpose large language models (LLMs) can reliably execute the complex clinical reasoning required for cardiovascular prognosis. We quantitatively assessed three LLMs-ChatGPT 4o, DeepSeek R1, and Grok 3-for predicting one-year major adverse cardiovascular events (MACEs), using 29 guideline-recommended features from 903 participants in the LM-ACS cohort and 64 participants in the MIMIC database. All models demonstrated significant risk overestimation and substantial output variability across ten independent runs. Although Grok 3 showed the highest initial accuracy, its average performance over all runs showed no significant difference. ROC analysis indicated that none of

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12874424 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature