Performance of ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1 in BI-RADS Category 4 Classification and Malignancy Prediction From Mammography Reports: Retrospective Diagnostic Study

Background Mammography is a key imaging modality for breast cancer screening and diagnosis, with the Breast Imaging Reporting and Data System (BI-RADS) providing standardized risk stratification. However, BI-RADS category 4 lesions pose a diagnostic challenge due to their wide malignancy probability range and substantial overlap between benign and malignant findings. Moreover, current interpretations rely heavily on radiologists’ expertise, leading to variability and potential diagnostic errors. Recent advances in large language models (LLMs), such as ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1, offer new possibilities for automated medical report interpretation. Objective This study aims to explore the feasibility of LLMs in evaluating the benign or malignant subcategories of BI-RADS category 4 lesions based on free-text mammography reports. Methods This retrospective, single-center study included 307 patients (mean age 47.25, 11.39 years) with BI-RADS category 4 mammography reports between May 2021 and March 2024. Three LLMs (ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1) classified BI-RADS 4 subcategories from the reports’ text only, whereas radiologists based their classifications on image review. Pathology served as the reference standard, and the reproducibility of LLMs’ predictions was assessed. The diagnostic performance of radiologists and LLMs was compared, and the internal reasoning behind LLMs’ misclassifications was analyzed. Results ChatGPT-4o demonstrated higher reproducibility than DeepSeek-R1 and Claude 3 Opus (Fleiss κ 0.850 vs 0.824 and 0.732, respectively). Although the overall accuracy of LLMs was lower than that of radiologists (senior: 74.5%; junior: 72.0%; DeepSeek-R1: 63.5%; ChatGPT-4o: 62.4%; Claude 3 Opus: 60.8%), their sensitivity was higher (senior: 80.7%; junior: 68.0%; DeepSeek-R1: 84.0%; ChatGPT-4o: 84.7%; Claude 3 Opus: 92.7%), while specificity remained lower (senior: 68.3%; junior: 76.1%; DeepSeek-R1: 43.0%; ChatGPT-4o: 40.1%; Claude 3 Opus: 28.9%). DeepSeek-R1 achieved the best prediction accuracy among LLMs with an area under the receiver operating characteristic curve of 0.64 (95% CI 0.57-0.70), followed by ChatGPT-4o (0.62, 95% CI 0.56-0.69) and Claude 3 Opus (0.61, 95% CI 0.54-0.67). By comparison, junior and senior radiologists achieved higher area under the receiver operating characteristic curves of 0.72 (95% CI 0.66-0.78) and 0.75 (95% CI 0.69-0.80), respectively. DeLong testing confirmed that all three LLMs performed significantly worse than both junior and senior radiologists (all P<.05), and no significant difference was observed between the two radiologist groups (P=.55). At the subcategory level, ChatGPT-4o yielded an overall F1-score of 47.6%, DeepSeek-R1 achieved 45.6%, and Claude 3 Opus achieved 36.2%. Conclusions LLMs are feasible for distinguishing between benign and malignant lesions in BI-RADS category 4, with good stability and high sensitivity, but relatively insufficient specificity. They show potential in screening and may assist radiologists in reducing missed diagnoses.

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