ArticleJMIR AI2025
Comparative Diagnostic Performance of a Multimodal Large Language Model Versus a Dedicated Electrocardiogram AI in Detecting Myocardial Infarction From Electrocardiogram Images: Comparative Study.
Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- ECG-based detection of occlusion myocardial infarction: a dedicated deep neural network versus multimodal large language models and physicians - a retrospective diagnostic accuracy study.BMC emergency medicine · 2026Article
- Performance of Vision-Enabled Large Language Models in Image-Based Electrocardiogram Interpretation: Exploratory Evaluation.Journal of medical Internet research · 2026Article
- ChatGPT's Limitations in Athlete ECG Interpretation: Evidence from a Multicenter Diagnostic Study.Journal of cardiovascular development and disease · 2026Article
- External validation of ECG artificial intelligence for emergency and cardiac assessment across a large-scale U.S. healthcare system.NPJ digital medicine · 2026Article
- Artificial Intelligence for Biomedical Diagnostics: Diagnostic Accuracy and Reliability of Multimodal Large Language Models in Electrocardiogram Interpretation.Life (Basel, Switzerland) · 2026Article
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6 authors.
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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. Methods: This retrospective study evaluated and compared AI models for classifying MI using a publicly available 12-lead ECG dataset from Pakistan, categorizing cases into MI-positive (239 images) and MI-negative (689 images). ChatGPT (GPT-4o, version November 20, 2024) and Gemini (Gemini 2.5 pro) were queried with 5 MI confidence options, whereas ECG Buddy for Microsoft Windows analyzed the images based on ST-elevation MI, acute coronary syndrome, and myocardial injury biomarkers. Results: Among 928 ECG recordings (239/928, 25.8% MI-positive), ChatGPT achieved an accuracy of 65.95% (95% CI 62.80-69.00), area under the curve (AUC) of 57.34% (95% CI 53.44-61.24), sensitivity of 36.40% (95% CI 30.30-42.85), and specificity of 76.2% (95% CI 72.84-79.33). With Gemini 2.5 Pro, accuracy dropped to 29.63% (95% CI 26.71-32.69), AUC to 51.63% (95% CI 50.22-53.04), and sensitivity rose to 97.07% (95% CI 94.06-98.81), but specificity fell sharply to 6.24% (95% CI 4.55-8.31). However, ECG Buddy reached an accuracy of 96.98% (95% CI 95.67-97.99), AUC of 98.8% (95% CI 98.3-99.43), sensitivity of 96.65% (95% CI 93.51-98.54), and specificity of 97.10% (95% CI 95.55-98.22). DeLong test confirmed that ECG Buddy significantly outperformed ChatGPT (all P<.001). In a qualitative error analysis of LLMs' diagnostic explanations, GPT-4o produced fully accurate explanations in only 5% of cases (2/40), was partially accurate in 38% (15/40), and completely inaccurate in 58% (23/40). By contrast, Gemini 2.5 Pro yielded fully accurate explanations in 32% of cases (12/37), was partially accurate in 14% (5/37), and completely inaccurate in 54% (20/37). Conclusions: LLMs, such as ChatGPT and Gemini, underperform relative to specialized tools such as ECG Buddy in ECG image-based MI diagnosis. Further training may improve LLMs; however, domain-specific AI remains essential for clinical accuracy. The high performance of ECG Buddy underscores the importance of specialized models for achieving reliable and robust diagnostic outcomes.
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