JRM
Journal of Radiology in Medicine

Journal of Radiology in Medicine is an international journal that published original research and articles in all areas of radiology. Its publishes original research articles, review articles, case reports, editorial commentaries, letters to the editor, educational articles, and conference/meeting announcements.

eISSN: 3023-6487
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Original Article
Feasibility of a multimodal large language model in interpreting plain radiographs of bone tumors: a pilot study
Abstract
Aims: To evaluate the diagnostic accuracy and clinical feasibility of a multimodal large language model (ChatGPT-5) in interpreting plain radiographs of bone tumors and differentiating between benign and malignant lesions.
Methods: This retrospective pilot study utilized 50 verified bone tumor cases (27 benign and 23 malignant) sourced from the Radiopaedia database. Anonymized radiographs were processed by ChatGPT-5 using a standardized zero-shot prompt in independent sessions to prevent contextual bias. Model performance was assessed based on the accuracy of the most likely diagnosis, the inclusion of correct diagnoses within the top three differentials, and benign–malignant classification metrics. Statistical analysis included the Clopper–Pearson binomial method for confidence intervals and McNemar’s exact test to evaluate improvements in diagnostic accuracy and potential systematic error asymmetry.
Results: The model achieved 100% accuracy in identifying the imaging modality and the affected bone. The accuracy for the single most likely diagnosis was 56.0% (95% CI: 41.3–70.0), which significantly increased to 70.0% (95% CI: 55.4–82.1) when two differential diagnoses were included (p=0.016). For benign–malignant classification, the model demonstrated an overall accuracy of 76.0%, with a high specificity of 96.3% but a notably limited sensitivity for malignancy at 52.2%. A statistically significant error asymmetry indicated a systematic tendency toward benign classification (p=0.006).
Conclusion: While ChatGPT-5 demonstrates proficiency in foundational radiographic identification, its low sensitivity for malignancy remains a critical limitation for independent clinical use. The results suggest that while multimodal LLMs may serve as promising educational or triage aids, they currently require rigorous human expert oversight to maintain diagnostic safety in image interpretation.

Keywords : Bone tumors, large language models, ChatGPT-5, artificial intelligence, radiology

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Article available in :
Volume 3, Issue 1, 2026
Page : 5-8
https://doi.org/10.51271/JRM-0042
Article Information
Received : 05 Şub 2026
Accepted : 04 Mar 2026
Published : 09 Mar 2026
Corresponding Author :

Samet Genez: Department of Radiology, Faculty of Medicine, Bolu Abant İzzet Baysal University, Bolu, Turkiye

[email protected]
Citation : Genez S, Özer H. Feasibility of a multimodal large language model in interpreting plain radiographs of bone tumors: a pilot study. J Radiol Med. 2026;3(1):5-8.

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