Performance of Large Language Models in Analyzing Common Hypertension Scenarios.

ABSTRACT:

Background

Hypertension, the leading cause of cardiovascular mortality, remains suboptimally controlled. Large language models (LLMs) could improve hypertension control by augmenting clinical decision-making, but their reliability for guideline-driven tasks is unverified. This study evaluated the accuracy and safety of hypertension management recommendations generated by 3 LLMs.

Methods

Fifty-one vignettes were constructed and submitted to the LLMs (GPT-4, Gemini, Medical Large Language Model [by Google; MedLM]) and a hypertension expert to generate the responses. Three blinded reviewers rated each response on a 4-point accuracy scale, a binary safety (safe/unsafe) scale, and attempted to identify the source (LLM versus expert) providing the response.

Results

GPT-4 ha

SUBMITTER: Zand J 

PROVIDER: S-EPMC12616542 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature