Improving large language models accuracy for aortic stenosis treatment via Heart Team simulation: a prompt design analysis.

ABSTRACT:

Aims

Large language models (LLMs) have shown potential in clinical decision support, but the influence of prompt design on their performance, particularly in complex cardiology decision-making, is not well understood.

Methods and results

We retrospectively reviewed 231 patients evaluated by our Heart Team for severe aortic stenosis, with treatment options including surgical aortic valve replacement, transcatheter aortic valve implantation, or medical therapy. We tested multiple prompt-design strategies using zero-shot (0-shot), Chain-of-Thought (CoT), and Tree-of-Thought (ToT) prompting, combined with few-shot prompting, free/guided-thinking, and self-consistency. Patient data were condensed into standardized vignettes and queried using GPT4-o (version 2024-05-13, OpenAI) 40

SUBMITTER: Garin D 

PROVIDER: S-EPMC12282391 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature