A Comparative Study of Predictive model (ECG Buddy) and ChatGPT-4o for Myocardial Infarction Diagnosis via ECG image Analysis: Performance, Accuracy, and Clinical Feasibility

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 Chat Generative Pre-trained Transformer (ChatGPT), 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.

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 2024-11-20) was queried with five MI confidence options, whereas ECG Buddy for Windows analyzed the images based on ST- elevation MI, acute coronary syndrome, and myocardial injury biomarkers.

Among 928 ECG recordings (25.8% MI-positive), ChatGPT achieved an accuracy of 65.95% (95% confidence interval [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 76.20% (95% CI: 72.84–79.33). 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’s test confirmed that ECG Buddy significantly outperformed ChatGPT (all P < .001). In an error analysis of 40 cases, ChatGPT provided clinically plausible explanations in only 7.5% of cases, whereas 35% were partially correct, 40% were completely incorrect, and 17.5% received no meaningful explanation.

LLMs such as ChatGPT underperform relative to specialized tools such as ECG Buddy in ECG image-based MI diagnosis. Further training may improve ChatGPT; 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. Competing Interest Statement Joonghee Kim, MD, PhD, developed the algorithm. He also founded a start-up company, ARPI Inc., where he serves as the CEO. Youngjin Cho, MD, PhD, and Dongbum Suh, MD, work for the company as research directors. Haemin Lee works for the company as a data scientist. The rest of the authors declare no conflict of interest. Funding Statement This research was partly supported by a grant from the Technological Innovation Research & Development Program (SCALEUP TIPS), funded by the Ministry of SMEs and Startups (grant number: RS-2024-00415492), and the Medical AI Clinic Program through the National IT Industry Promotion Agency, funded by the Ministry of Science Information Communication and Technology (grant number: H0904-24-1002). Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Khan AH, Hussain M. ECG images dataset of cardiac patients. Mendeley Data. 2021;V2. doi:10.17632/gwbz3fsgp8.2 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data produced in the present study are available upon reasonable request to the authors