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Extraction of clinical findings from mammography and breast ultrasound reports: a comparison between specialists and Artificial Intelligence — arXiv:2609.31974

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.31974
License (as archived): CC0-1.0
Archived: 2026-10-07T20:59:44.230Z
SHA-256 of the archived text: 8cbf5d350cdabb8e50402f048ffdea180cfba021aeef50c79f27d200104da184
Stable URL of this page: https://metraipolis.com/documento/dc_19fc689409cf

How to cite

Extraction of clinical findings from mammography and breast ultrasound reports: a comparison between specialists and Artificial Intelligence — arXiv:2609.31974 — arXiv (en), https://arxiv.org/abs/2609.31974 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_19fc689409cf (Metraipolis archive dc_19fc689409cf, sha256 8cbf5d350cdabb8e50402f048ffdea180cfba021aeef50c79f27d200104da184)

BibTeX

@misc{dc19fc689409cf,
  title = {Extraction of clinical findings from mammography and breast ultrasound reports: a comparison between specialists and Artificial Intelligence — arXiv:2609.31974},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.31974},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_19fc689409cf; sha256 8cbf5d350cdabb8e50402f048ffdea180cfba021aeef50c79f27d200104da184},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.31974. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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 incorrectly recorded during manual extraction, demonstrating its potential as a complementary verification tool alongside specialists. The results confirm the hypothesis that LLMs, when instructed through Prompt Engineering, can achieve performance comparable to or superior to manual extraction by health professionals.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-29 in cs.AI).
Categorie arXiv: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.31974 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T20:29:02.192Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).