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Comparative Analysis of M4CXR, an LLM-Based Chest X-Ray Report Generation Model, and ChatGPT in Radiological
Ro Woon Lee1, Kyu Hong Lee1, Jae Sung Yun2
1Department of Radiology, Inha University College of Medicine, Incheon 22332, Republic of Korea.
Journal of Clinical Medicine
|December 17, 2024
Summary
M4CXR, a specialized AI tool, demonstrated superior diagnostic accuracy and fewer hallucinations in chest X-ray interpretation compared to ChatGPT-4o. Both AI tools show potential to augment, not replace, human expertise in radiology.
Area of Science:
- Artificial Intelligence in Medical Imaging
- Radiology and Diagnostic Imaging
- Machine Learning in Healthcare
Background:
- Investigated diagnostic capabilities of M4CXR and ChatGPT-4o for chest X-ray interpretation.
- M4CXR is a specialized cloud-based AI for radiology reports; ChatGPT-4o is a general AI model.
- Assessed AI potential in settings with limited radiological expertise.
Purpose of the Study:
- Compare diagnostic performance of M4CXR and ChatGPT-4o on chest X-rays.
- Evaluate accuracy, false findings, localization, count, and hallucinations.
- Quantify interobserver agreement using Cohen's kappa coefficient.
Main Methods:
- Evaluated 826 anonymized chest X-ray images from Inha University Hospital.
- Two experienced radiologists independently assessed AI tool performance.
- Focused on diagnostic accuracy, false findings, location accuracy, count accuracy, and hallucinations.
Main Results:
- M4CXR outperformed ChatGPT-4o across all metrics.
- M4CXR achieved 60-62% diagnostic accuracy acceptability vs. ChatGPT's 42-45%.
- M4CXR showed superior anatomical localization (76-77.5%) and fewer hallucinations than ChatGPT (36-36.5%).
Conclusions:
- AI tools like M4CXR and ChatGPT-4o show complementary potential in medical diagnostics.
- M4CXR demonstrates stronger performance in specialized radiological analysis.
- Combined AI and clinical judgment can enhance diagnostic workflows and patient care.
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