Extracting structured data from unstructured breast imaging reports with transformer-based models

Generative models, particularly BioGPT, offer a robust and scalable approach to automating the extraction of structured information from unstructured breast imaging reports. Their superior performance, combined with their ability to handle multiple tasks concurrently, highlights their potential to reduce the manual effort required for clinical data curation and to enable the efficient integration of imaging data into research and clinical workflows.

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Publication Details

Journal
Frontiers in Digital Health
Published
2026-01-09
DOI
https://doi.org/10.3389/fdgth.2025.1718330
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
0.00

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article

Extracting structured data from unstructured breast imaging reports with transformer-based models

Francisco Javier Díez, Iñigo Bermejo Delgado, Mikel Carrilero-Mardones, Jorge Pérez-Martín
Frontiers in Digital Health
AI in cancer detection
article

Extracting structured data from unstructured breast imaging reports with transformer-based models

Francisco Javier Díez, Iñigo Bermejo Delgado, Mikel Carrilero-Mardones, Jorge Pérez-Martín
article en

Abstract

Generative models, particularly BioGPT, offer a robust and scalable approach to automating the extraction of structured information from unstructured breast imaging reports. Their superior performance, combined with their ability to handle multiple tasks concurrently, highlights their potential to reduce the manual effort required for clinical data curation and to enable the efficient integration of imaging data into research and clinical workflows.

Frontiers in Digital HealthVol. 7
Universidad Nacional de Educación a Distancia (ES), Hasselt University (BE)
Universidad Nacional de Educación a Distancia, Universiteit Hasselt
Peace, Justice and strong institutions
Openalex Percentile: Top 93%
AI in cancer detection
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