Evaluating the appropriateness and safety of generative AI in delivering lifestyle guidance for atrial fibrillation patients.

ABSTRACT: Lifestyle factors play a major role in atrial fibrillation (AF) incidence, but the effectiveness of lifestyle counseling varies among individuals. Due to limited consultation time, physicians often provide only brief guidance, leaving patients to manage changes on their own. This study assessed the clinical utility of three Large Language Models (LLMs) for delivering accurate and personalized lifestyle guidance: (1) GPT-4o, (2) a retrieval-augmented model using a curated Q&A database (DB GPT), and (3) a modular RAG model retrieving evidence from PubMed (PubMed GPT). Sixty-six questions from 16 AF patients were categorized into exercise, diet, lifestyle, and other domains. Five experienced electrophysiologists independently evaluated LLM-generated lifestyle guidance and physician-provided c

SUBMITTER: Makino M 

PROVIDER: S-EPMC12855944 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature