ArticleAbdominal radiology (New York)2026
Diagnostic performance of large language models for discrepancy detection in non-English resident-authored abdominal CT and MRI reports: a Turkish-language evaluation.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo evaluate the diagnostic performance of large language models (LLMs) for detecting clinically meaningful discrepancies in non-English, resident-authored abdominal CT and MRI reports, using Turkish-language reports as a test setting, and to compare the educational quality of model-generated feedback.
methodsThis retrospective study included 488 abdominal imaging reports, comprising 317 CT and 171 MRI examinations, each with a resident-authored preliminary report and an attending-approved final report. Two abdominal radiologists established an expert consensus reference standard using both report pairs and corresponding imaging studies. Three LLMs-GPT-5.3, Claude 4.6 Sonnet, and Gemini 3.0 Pro-were prompted to detect and classify discrepancies and generate brief educational feedback. Importantly, models evaluated text-based report pairs without direct image access. Primary outcomes were event-level discrepancy detection sensitivity and false-positive behavior; educational feedback was assessed using a 5-point Likert scale.
resultsThe reference standard identified 776 clinically meaningful discrepancies in 390 reports; 98 reports were error-free. Event-level sensitivity differed significantly among models (p < 0.001). Claude 4.6 Sonnet achieved the highest overall sensitivity (93.4%), followed by GPT-5.3 (91.9%) and Gemini 3.0 Pro (72.6%). Claude also showed the highest sensitivity for major discrepancies (97.8%) and the lowest false-positive rate (0.6%). Gemini 3.0 Pro achieved the highest educational feedback score (4.60 ± 0.65), followed by GPT-5.3 (4.41 ± 0.68) and Claude 4.6 Sonnet (3.95 ± 0.80; p < 0.001).
conclusionUnder a single structured prompt, LLMs showed model-dependent performance in the text-based comparison of Turkish preliminary and final abdominal radiology reports. These single-center findings do not establish image-based diagnostic accuracy or readiness for autonomous clinical use and require prospective multicenter validation.
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