Comparative Diagnostic Performance of a Multimodal Large Language Model Versus a Dedicated Electrocardiogram AI in Detecting Myocardial Infarction From Electrocardiogram Images: Comparative Study.

ABSTRACT:

Background

Accurate and timely electrocardiogram (ECG) interpretation is critical for diagnosing myocardial infarction (MI) in emergency settings. Recent advances in multimodal large language models (LLMs), such as ChatGPT (OpenAI) and Gemini (Google DeepMind), have shown promise in clinical interpretation for medical imaging. However, whether these models analyze waveform patterns or simply rely on text cues remains unclear, underscoring the need for direct comparisons with dedicated ECG artificial intelligence (AI) tools.

Objective

This study aimed to evaluate the diagnostic performance of ChatGPT and Gemini, a general-purpose LLM, in detecting MI from ECG images and to compare its performance with that of ECG Buddy (ARPI Inc), a dedicated AI-driven ECG analysis tool.

M

SUBMITTER: Lee H 

PROVIDER: S-EPMC12443349 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature