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

Turay Cesur1, Yasin Celal Gunes2, Eren Camur3

  • 1Department of Radiology, Mamak State Hospital, Ankara, Türkiye.

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|October 6, 2025
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Summary
This summary is machine-generated.

Large Language Models (LLMs) show promise in cardiac radiology, with ChatGPT-4o significantly improving general radiologists' diagnostic accuracy and differential diagnosis scores. Claude 3 Opus demonstrated superior performance among LLMs.

Keywords:
cardiac imagingchatGPT-4oclaude 3 opusclaude 3.5 sonnetdiagnostic improvement

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Radiology and Diagnostic Imaging
  • Cardiovascular Radiology

Background:

  • Evaluating the diagnostic capabilities of Large Language Models (LLMs) and human radiologists in cardiac radiology is crucial.
  • Assessing the impact of AI assistance, specifically ChatGPT-4o, on diagnostic performance is a key area of research.

Purpose of the Study:

  • To compare the diagnostic accuracy and differential diagnosis capabilities of 12 LLMs against human radiologists (one cardiac, three general) in cardiac radiology.
  • To investigate the effect of ChatGPT-4o assistance on the performance of general radiologists.

Main Methods:

  • Utilized 80 "Cardiac Case of the Month" cases from the Society of Thoracic Radiology website.
  • LLMs and one radiologist received text-based case information; other radiologists reviewed cases visually, with and without ChatGPT-4o assistance.
  • Diagnostic accuracy and differential diagnosis (DDx) scores were statistically analyzed using various tests (χ², Kruskal-Wallis, Wilcoxon, McNemar, Mann-Whitney U).

Main Results:

  • Unassisted cardiac radiologist accuracy was 72.5%. ChatGPT-4o improved general radiologists' accuracy significantly (e.g., Radiologist-I from 53.8% to 70.0%).
  • ChatGPT-4o significantly enhanced all radiologists' DDx scores. Radiologist-I's assisted performance neared the unassisted cardiac radiologist's level.
  • Among LLMs, Claude 3 Opus and Claude 3.5 Sonnet achieved the highest accuracy (81.3%). Claude 3 Opus led in DDx scores, outperforming all models and a radiologist.

Conclusions:

  • ChatGPT-4o shows potential as a diagnostic support tool, enhancing general radiologists' performance in cardiac imaging.
  • Further research is necessary to determine the optimal clinical integration of AI tools like ChatGPT-4o in radiology practices.