Extraction of clinical findings from mammography and breast ultrasound reports: a comparison between specialists and Artificial Intelligence

Lorenzo Farias, Hanna Reckziegel, Daniela Duarte da Silva Bagatini, Daniel Schulz, Gabriela de Andrade Monteiro +6

Breast cancer is the leading cause of cancer-related death among women in Brazil, and the time between the request and the release of mammography reports directly influences adherence to screening, making the agility in processing these reports a critical factor for early diagnosis. In this context, this study compares the performance of a Large Language Model (LLM) with manual extraction performed by a team of health researchers in identifying clinical findings from mammography and breast ultrasound reports written in Brazilian Portuguese. Named Entity Recognition (NER) was applied through Prompt Engineering using a few-shot strategy, employing the Gemini 2.5 Flash model, selected from preliminary exploratory tests with four candidate models. The Gemini 2.5 Flash model demonstrated the best performance, achieving a Macro F1 of 0.91 and a Micro F1 of 0.98. The subjective validation, in which 29 exams of different formats were evaluated by health researchers using a Likert scale, yielded an agreement index of 93.1%. The model outperformed human extraction in overall Macro F1 (0.91 vs. 0.72), as in four reports the model correctly identified information that had been omitted or…