Evaluation study shows limited utility of GPT-4 in assigning BI-RADS categories to breast imaging reports, highlighting frequent errors and poor reproducibility.
The large language model GPT-4 showed limited utility in generating BI-RADS assessment categories for factitious breast imaging reports containing findings and impression sections, with frequent incorrect BI-RADS category assignments and poor reproducibility in assigned BI-RADS categories across independent tests for the same report using the same prompt.
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Haver et al. (2024) studied this question.
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