AI-Assisted Patient Education on Myocardial Infarction in the Emergency Department: Accuracy and Reliability Analysis of ChatGPT, Claude, and Gemini

Purpose Acute myocardial infarction (AMI) is a leading global cause of morbidity and mortality. Large language models (LLMs) have emerged as innovative tools for patient education. This study evaluated the performance of three LLMs in delivering accurate, reliable, and readable patient information regarding AMI. Methods In this cross-sectional study (February–March 2025), a clinical case of a patient with an inferior STEMI ECG was presented to three LLMs: ChatGPT-4o, Claude 3.7 Sonnet, and Gemini Advanced 2.0 Flash. Each model answered 30 patient-focused questions across three domains: general disease knowledge, diagnostic processes, and treatment approaches. Responses were assessed by four emergency medicine associate professors (10–20 years of experience) using a 5-point Likert scale for accuracy, DISCERN and EQIP tools for reliability and quality, and standard readability indices. Results ChatGPT-4o achieved the highest accuracy score (4.38 ± 0.38), followed by Claude 3.7 (4.09 ± 0.55) and Gemini 2.0 (3.92 ± 0.41). ChatGPT-4o performed significantly better in general information and diagnostic domains, while Claude 3.7 excelled in treatment-related content. All models showed limitations in technical domains such as ECG interpretation. Claude 3.7 produced the most readable content. ChatGPT-4.0 scored “excellent” on the DISCERN scale; all models were rated as “good quality with minor shortcomings” on EQIP. Conclusion LLMs show promise in supporting patient education on AMI. While ChatGPT-4o offers superior accuracy and reliability, Claude 3.7 enhances accessibility through clearer language. However, physician oversight remains essential, particularly in high-stakes clinical settings.