ArticleScientific reports2025
Evaluating the appropriateness and safety of generative AI in delivering lifestyle guidance for atrial fibrillation patients.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Lessons from Smart Cart 2.0: Considerations for Integrating Purchasing Data into Healthy Eating Interventions with AI Approaches.The Journal of nutrition · 2026Article
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Authors and funding
5 authors.
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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 counseling responses using ten dimensions. GPT-4o demonstrated a comparable level of scientific consensus to electrophysiologists, while achieving a lower error rate and significantly higher levels of specialized content, empathy, and helpfulness. DB GPT and PubMed GPT showed similar error rates, proportions of specialized content, empathy, and helpfulness compared to electrophysiologists, but exhibited strengths in specialized content in exercise-related and accuracy in diet-related dimensions. These findings suggest that integrating complementary model strengths may help develop safer and more reliable medical AI systems.
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