Performance of ChatGPT-4o in Determining Radiology–Pathology Concordance and Management Recommendations Following Image-Guided Breast Biopsies

<b>Background:</b> Determining radiology-pathology concordance after breast biopsies is critical to ensuring appropriate patient management. However, expertise and multidisciplinary input are not universally accessible. <b>Purpose:</b> To evaluate the performance of a large language model, ChatGPT-4o, in determining the radiology-pathology concordance of breast biopsies and suggesting subsequent management steps. <b>Methods:</b> A retrospective single-center study analyzed 244 cases of image-guided breast biopsies of women. ChatGPT-4o assessed de-identified radiology and pathology reports for concordance and recommended management. Radiologist assessments served as the reference standard with final surgical pathology and 2-year imaging follow-up serving as gold standards when applicable. Concordance rates, management recommendations, and diagnostic agreement with the gold standard were compared using statistical tests, including McNemar's, chi-square, Fisher-Freeman-Halton, and Cohen's kappa. <b>Results:</b> ChatGPT-4o achieved a concordance rate of 98.8% vs. 98.0% for radiologists (<i>p</i> = 0.625) and demonstrated high diagnostic agreement with the gold standard (kappa = 0.947, <i>p</i> < 0.001). ChatGPT-4o favored imaging follow-up more than radiologists (49.2% vs. 41.8%, <i>p</i> < 0.001) and surgical management less frequently (41.8% vs. 46.7%). <b>Conclusions:</b> ChatGPT-4o demonstrated diagnostic performance comparable to radiologists with breast imaging subspecialities in evaluating breast biopsy concordance. Its slightly more conservative management approach may enhance shared decision-making in resource-limited settings.