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Journals / Uludağ Üniversitesi Tıp Fakültesi Dergisi / 2026 / Cilt: 52

Diagnostic Performance of Multimodal Large Language Models in Assigning Bone-RADS Categories on CT

Multimodal Büyük Dil Modellerinin BT’de Bone-RADS Klasifikasyonundaki Tanısal Performansı

Hasan Emin Kaya(Bursa Uludağ University, School of Medicine, Department of Radiology, Bursa, Türkiye.)0000-0002-7411-4102
Abdullah Enes Ataş(Necmettin Erbakan University, Meram School of Medicine, Department of Radiology, Konya, Türkiye.)0000-0001-6623-3024
Pages
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Publication Date
2026-02-10
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In conclusion, general-purpose MLLMs cannot yet replace trained radiologists for Bone-RADS classification, though they may still aid routine clinical practice.

📌 3 Key Takeaways

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Objective & Hypothesis:The aim of the study is to assess the performance of multimodal large language models (MLLMs) in assigning Bone-RADS categories to bone lesions identified on CT images.
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Key Findings & Data:An MSK radiologist selected one representative slice for 50 bone lesions seen on CT studies and assigned reference Bone-RADS categories using clinical records.
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Conclusion & Impact:In conclusion, general-purpose MLLMs cannot yet replace trained radiologists for Bone-RADS classification, though they may still aid routine clinical practice.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:MBDM · BDM · Bone-RADS

Abstract

The aim of the study is to assess the performance of multimodal large language models (MLLMs) in assigning Bone-RADS categories to bone lesions identified on CT images. An MSK radiologist selected one representative slice for 50 bone lesions seen on CT studies and assigned reference Bone-RADS categories using clinical records. Three raters categorized each case: an abdominal radiologist, OpenAI ChatGPT 5, and Google Gemini 2.5 Pro. Accuracy was defined as the correctly labeled Bone-RADS 1 and 4 cases and compared using McNemar test. Agreement with the reference was assessed using weighted Cohen’s κ with 95% CIs; pairwise κ differences were tested via bootstrap. Reference categories were Bone-RADS 1, n=23; 2, n=4; 3, n=0; 4, n=23. Accuracy was 84.8% (39/46) for the radiologist, 78.3% (36/46) for Gemini, and 65.2% (30/46) for ChatGPT. The radiologist outperformed ChatGPT (p=0.012); differences between the radiologist vs Gemini (p=0.604) and Gemini vs ChatGPT (p=0.360) were not significant. The radiologist achieved the highest agreement with the reference standard (κ = 0.715, 95% CI: [0.543-0.887]), followed by Gemini (κ = 0.542, 95% CI: [0.313-0.770]) and ChatGPT (κ = 0.292, 95% CI: [0.104-0.479]). Bootstrap comparisons showed that the radiologist’s κ was higher than ChatGPT’s (95% CI for difference, 0.140-0.675), while radiologist vs Gemini (−0.113-0.434) and Gemini vs ChatGPT (−0.041-0.522) were not significant. In conclusion, general-purpose MLLMs cannot yet replace trained radiologists for Bone-RADS classification, though they may still aid routine clinical practice.

Özet

Çalışmamızın amacı multimodal büyük dil modellerinin (MBDM) BT görüntülerinde tespit edilen soliter kemik lezyonlarına Bone-RADS kategorileri atamadaki performanslarını değerlendirmektir. Hastanemizin PACS’ı soliter kemik lezyonu içeren BT tetkikleri için taranmıştır (Ağustos 2024-Ağustos 2025). Her lezyon için bir kas-iskelet radyoloğu tarafından kitleyi en iyi temsil eden bir kesit seçilmiş ve lezyonlara birer referans Bone-RADS skoru atanmıştır. Daha sonra bir abdominal radyolog, ChatGPT 5 ve Gemini 2.5 Pro aynı vakaları kategorilemiştir. Doğruluk, doğru şekilde kategorize edilen Bone-RADS 1 ve 4 vakaları olarak tanımlanmış ve McNemar testi kullanılarak karşılaştırılmıştır. Referansla uyum, ağırlıklı Cohen κ katsayısı kullanılarak değerlendirilmiş ve bootstrap yöntemi ile karşılaştırılmıştır. Referans kategorileri şu şekilde belirlenmiştir: Bone-RADS 1, n=23; 2, n=4; 3, n=0; 4, n=23. Doğruluk, radyolog için %84,8 (39/46), Gemini için %78,3 (36/46) ve ChatGPT için %65,2 (30/46) olarak bulunmuştur. Radyoloğun, ChatGPT'den daha iyi performans gösterdiği (p=0,012); radyolog ile Gemini (p=0,604) ve Gemini ile ChatGPT (p=0,360) arasındaki farkların anlamlı olmadığı görülmüştür. Radyolog, referans standardı ile en yüksek uyumu elde etmiş (κ = 0,715, %95 GA: [0,543-0,887]), bunu Gemini (κ = 0,542, %95 GA: [0,313-0,770]) ve ChatGPT (κ = 0,292, %95 GA: [0,104-0,479]) izlemiştir. Bootstrap ile yapılan karşılaştırmalar, radyoloğun κ değerinin ChatGPT'den daha yüksek olduğunu göstermiş (%95 GA: 0,140-0,675), ancak radyolog ile Gemini (%95 GA: −0,113-0,434) ve Gemini ile ChatGPT (%95 GA: −0,041-0,522) arasındaki fark anlamlı bulunmamıştır. Sonuç olarak genel amaçlı MBDM'ler henüz Bone-RADS kategorizasyonu için eğitimli radyologların yerini tutabilecek durumda görünmemekle beraber bu modellerin günlük pratikte radyologlara yardımcı olabileceği düşünülmektedir.

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Cite This Article

Kaya, H. E., Ataş, A. E., *, H. E. K. (2026). Diagnostic Performance of Multimodal Large Language Models in Assigning Bone-RADS Categories on CT. Uludağ Üniversitesi Tıp Fakültesi Dergisi, Cilt: 52. https://doi.org/10.32708/uutfd.1804768

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