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Comparative assessment of DeepSeek-R1 and GPT-4 for structured ultrasound reporting of adnexal masses
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DOI：10.3389/fonc.2026.1904488.png)
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Abstract
En 中文
ObjectiveThis study primarily evaluated the ability of two large language models (DeepSeek-R1 and GPT-4) to generate structured ultrasound reports from free-text adnexal mass reports. Secondarily; we assessed their accuracy in O-RADS classification and management recommendations; with an exploratory analysis of their performance in benign versus malignant discrimination.MethodsThis study included 215 free-text ultrasound reports of adnexal masses (AMs) between July 2024 and March 2025. Each report was processed three times per model; majority voting was used to determine the final output. Three senior radiologists; blinded to model identity; evaluated structured reports against a predefined template; O-RADS categories; and management guidelines. Histopathology served as the reference standard for benign/malignant discrimination; while expert consensus served as the reference for the other endpoints.ResultsGPT-4 showed numerically better performance than DeepSeek-R1 in generating structured reports (99.5% vs. 96.7%; p = 0.07).DeepSeek-R1 outperformed GPT-4 in O-RADS accuracy (64.4% vs. 52.9%; p < 0.001) and appropriate management recommendations (66.4% vs. 58.0%; p = 0.007). Both models demonstrated good discrimination ability for benign versus malignant classification (AUC > 0.80 for both).ConclusionBoth DeepSeek-R1 and GPT-4 demonstrate potential for automating structured ultrasound reporting of AMs; with DeepSeek-R1 showing superior performance in classification and recommendations. However; their diagnostic performance in discriminating benign from malignant masses; while promising; requires further refinement before independent clinical application. These findings support their role as assistive tools; pending integration with image data and prospective validation.
Keywords:
adnexal masses
GPT-4
ultrasound diagnosis
DeepSeek-R1
structured reports
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