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GPT-4 vs. radiologists: who advances mediastinal tumor classification better across report quality levels? a cohort
Ru Wen1, Xiaoming Li1, Kang Chen1
17T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, Southwest Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Background:
Accurate mediastinal tumor classification is crucial for treatment planning, but diagnostic performance varies with radiologists' experience and report quality.
Purpose:
To evaluate generative pretrained transformer's (GPT-4's) diagnostic accuracy in classifying mediastinal tumors from radiological reports compared to radiologists of different experience levels using radiological reports of varying quality.
Materials And Methods:
We conducted a retrospective study of 1494 patients from five tertiary hospitals with mediastinal tumors diagnosed via chest CT and pathology. Radiological reports were categorized into low-, medium-, and high-quality based on predefined criteria assessed by experienced radiologists. Six radiologists (two residents, two attending radiologists, and two associate senior radiologists) and GPT-4 evaluated the chest CT reports. Diagnostic performance was analyzed overall, by report quality, and by tumor type using Wald χ2 tests and 95% CIs calculated via the Wilson method.
Results:
GPT-4 achieved an overall diagnostic accuracy of 73.3% (95% CI: 71.0-75.5), comparable to associate senior radiologists (74.3%, 95% CI: 72.0-76.5; P >0.05). For low-quality reports, GPT-4 outperformed associate senior radiologists (60.8% vs. 51.1%, P <0.001). In high-quality reports, GPT-4 was comparable to attending radiologists (80.6% vs.79.4%, P >0.05). Diagnostic performance varied by tumor type: GPT-4 was comparable to radiology residents for neurogenic tumors (44.9% vs. 50.3%, P >0.05), similar to associate senior radiologists for teratomas (68.1% vs. 65.9%, P >0.05), and superior in diagnosing lymphoma (75.4% vs. 60.4%, P <0.001).
Conclusion:
GPT-4 demonstrated interpretation accuracy comparable to Associate Senior Radiologists, excelling in low-quality reports and outperforming them in diagnosing lymphoma. These findings underscore GPT-4's potential to enhance diagnostic performance in challenging diagnostic scenarios.
Summary:
In this retrospective study involving 1494 chest CT reports of different quality from five tertiary hospitals, GPT-4 demonstrated diagnostic accuracy comparable to Associate Senior Radiologists in classifying mediastinal tumors from chest CT reports, excelling in low-quality reports and outperforming Associate Senior Radiologists in diagnosing specific tumor types like lymphoma, showcasing its potential to enhance diagnostic performance in challenging scenarios.
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