Evaluating the Diagnostic Accuracy of ChatGPT-40 for Classifying Multimodal Musculoskeletal Masses: A Comparative Study with Human Raters Bewertung der diagnostischen Genauigkeit von ChatGPT-4.0 bei der Klassifikation multimodaler muskuloskelettaler Läsionen: eine vergleichende Studie mit menschlichen Auswertern
RoFo Fortschritte auf dem Gebiet der Rontgenstrahlen und der Bildgebenden Verfahren, cilt.198, sa.3, ss.358-365, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 198 Sayı: 3
- Basım Tarihi: 2026
- Doi Numarası: 10.1055/a-2594-7085
- Dergi Adı: RoFo Fortschritte auf dem Gebiet der Rontgenstrahlen und der Bildgebenden Verfahren
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE
- Sayfa Sayıları: ss.358-365
- Anahtar Kelimeler: Clinical Decision Support, Diagnostic Accuracy, Artificial Intelligence, Musculoskeletal Tumors
- Sağlık Bilimleri Üniversitesi Adresli: Evet
Özet
Purpose Novel artificial intelligence tools have the potential to significantly enhance productivity in medicine, while also maintaining or even improving treatment quality. In this study, we aimed to evaluate the current capability of ChatGPT-4.0 to accurately interpret multimodal musculoskeletal tumor cases. Materials and Methods We created 25 cases, each containing images from X-ray, computed tomography, magnetic resonance imaging, or scintigraphy. ChatGPT-4.0 was tasked with classifying each case using a six-option, two-choice question, where both a primary and a secondary diagnosis were allowed. For performance evaluation, human raters also assessed the same cases. Results When only the primary diagnosis was taken into account, the accuracy of human raters was greater than that of ChatGPT-4.0 by a factor of nearly 2 (87% vs. 44%). However, in a setting that also considered secondary diagnoses, the performance gap shrank substantially (accuracy: 94% vs. 71%). Power analysis relying on Cohen's w confirmed the adequacy of the sample set size (n: 25). Conclusion and Key Points The tested artificial intelligence tool demonstrated lower performance than human raters. Considering factors such as speed, constant availability, and potential future improvements, it appears plausible that artificial intelligence tools could serve as valuable assistance systems for doctors in future clinical settings. Key Points ChatGPT-4.0 classifies musculoskeletal cases using multimodal imaging inputs. Human raters outperform AI in primary diagnosis accuracy by a factor of nearly two. Including secondary diagnoses improves AI performance and narrows the gap. AI demonstrates potential as an assistive tool in future radiological workflows. Power analysis confirms robustness of study findings with the current sample size.