Diagnostic performance of ChatGPT in adnexal mass classification: A comparative study with frozen section and final histopathology

Objective: To evaluate the diagnostic performance of ChatGPT in classifying adnexal masses as benign, borderline, or malignant, and to compare its performance with intraoperative frozen-section analysis, using final histopathology as the reference standard. Materials and Methods: This retrospective, observational, comparative study included 259 patients who underwent surgery for ovarian masses between January 2021 and July 2025. Preoperative clinical data, laboratory parameters, and ultrasonographic findings were recorded. ChatGPT assessments were performed using structured clinical inputs without prompt optimization. Frozen section diagnoses and ChatGPT classifications were compared with final histopathological results. Diagnostic performance metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), Cohen's kappa coefficient, and area under the curve (AUC), were calculated. Results: According to final histopathology, 83.4% of cases were benign, 7.7% were borderline, and 8.9% were malignant. Frozen section analysis demonstrated high diagnostic accuracy, with sensitivity and specificity of 94.4% and 100%, respectively, when borderline cases were excluded. In comparison, ChatGPT showed a sensitivity of 90.0% and a specificity of 92.5%. The PPV for ChatGPT (56.3%) was markedly lower than that for frozen section (100%), whereas NPV values were comparable (98.9% vs. 99.5%). When borderline tumors were classified as malignant, ChatGPT's performance declined further, with a sensitivity of 74.4%, a specificity of 79.6%, and a PPV of 42.1%. Receiver operating characteristic analysis showed AUCs of 0.967 for the frozen section and 0.893 for ChatGPT. ChatGPT demonstrated a higher rate of false-positive classifications, particularly in the borderline and malignant categories. Conclusion: Frozen section analysis remains a highly reliable method for intraoperative evaluation of ovarian tumors. Although ChatGPT demonstrates good discriminative ability and high NPV, its lower specificity and PPV limit its role as a standalone diagnostic tool. ChatGPT may serve as a complementary decision-support tool, particularly in ruling out malignancy, but cannot replace histopathological evaluation.