Empowering Radiologists with ChatGPT-4o: Comparative Evaluation of Large Language Models and Radiologists in Cardiac Cases

ABSTRACT Purpose This study evaluated the diagnostic accuracy and differential diagnosis capabilities of 12 Large Language Models (LLMs), one cardiac radiologist, and three general radiologists in cardiac radiology. The impact of ChatGPT-4o assistance on radiologist performance was also investigated. Materials and Methods We collected publicly available 80 “Cardiac Case of the Month’’ from the Society of Thoracic Radiology website. LLMs and Radiologist-III were provided with text-based information, whereas other radiologists visually assessed the cases with and without ChatGPT-4o assistance. Diagnostic accuracy and differential diagnosis scores (DDx Score) were analyzed using the chi-square, Kruskal-Wallis, Wilcoxon, McNemar, and Mann-Whitney U tests. Results The unassisted diagnostic accuracy of the cardiac radiologist was 72.5%, General Radiologist-I was 53.8%, and General Radiologist-II was 51.3%. With ChatGPT-4o, the accuracy improved to 78.8%, 70.0%, and 63.8%, respectively. The improvements for General Radiologists-I and II were statistically significant (P≤0.006). All radiologists’ DDx scores improved significantly with ChatGPT-4o assistance (P≤0.05). Remarkably, Radiologist-I’s GPT-4o-assisted diagnostic accuracy and DDx Score were not significantly different from the Cardiac Radiologist’s unassisted performance (P>0.05). Among the LLMs, Claude 3.5 Sonnet and Claude 3 Opus had the highest accuracy (81.3%), followed by Claude 3 Sonnet (70.0%). Regarding the DDx Score, Claude 3 Opus outperformed all models and Radiologist-III (P<0.05). The accuracy of the general radiologist-III significantly improved from 48.8% to 63.8% with GPT4o-assistance (P<0.001). Conclusion ChatGPT-4o may enhance the diagnostic performance of general radiologists for cardiac imaging, suggesting its potential as a valuable diagnostic support tool. Further research is required to assess its clinical integration.