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Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70924, first published .
Using a Large Language Model for Breast Imaging Reporting and Data System Classification and Malignancy Prediction to Enhance Breast Ultrasound Diagnosis: Retrospective Study

Using a Large Language Model for Breast Imaging Reporting and Data System Classification and Malignancy Prediction to Enhance Breast Ultrasound Diagnosis: Retrospective Study

Using a Large Language Model for Breast Imaging Reporting and Data System Classification and Malignancy Prediction to Enhance Breast Ultrasound Diagnosis: Retrospective Study

Journals

  1. Tekcan Sanli D, Sanli A, Ozmen G, Ozmen A, Cihan I, Kurt A, Esmerer E. Interpreting BI-RADS-Free Breast MRI Reports Using a Large Language Model: Automated BI-RADS Classification From Narrative Reports Using ChatGPT. Academic Radiology 2025;32(12):7108 View
  2. Erkal D, Tonkaz M, Bekci T, Tonkaz G. Comparative Evaluation of BI-RADS Classification, Clinical Management, and Diagnostic Performance in Breast Ultrasound Reports Using ChatGPT-4o and DeepSeek-V3. Seminars in Ultrasound, CT and MRI 2025 View
  3. Dai X, Ke M, Xie D, Mei M, Wei S, Dai Y, Yan R. Performance of ChatGPT-4o, Claude 3 Opus, and DeepSeek-R1 in BI-RADS Category 4 Classification and Malignancy Prediction From Mammography Reports: Retrospective Diagnostic Study. JMIR Medical Informatics 2025;13:e80182 View
  4. Carrilero-Mardones M, Pérez-Martín J, Díez F, Bermejo Delgado I. Extracting structured data from unstructured breast imaging reports with transformer-based models. Frontiers in Digital Health 2026;7 View
  5. Akdal Dolek B, Besler M. Evaluation of large language models in assigning PI-RADS v2.1 categories for prostate MRI reports. BMC Urology 2026;26(1) View
  6. Abbasian Ardakani A, Mohammadi A, Kuzan T, Kuzan B, Khorshidi H, Ghorbani A, Mohebbi A, Faeghi F, Hatamikia S, Acharya U. Ultrasound-based Detection and Malignancy Prediction of Breast Lesions Eligible for Biopsy: A Multi-center Clinical-scenario Study Using Nomograms, Large Language Models, and Radiologist Evaluation. Academic Radiology 2026 View
  7. Sun Y, Shen H, Zhang L, Jiang Y, Zheng Q, Du L, He M, Wu L, Xie H. Transforming adnexal mass assessment: how large language model improve ovarian-adnexal reporting and data system interpretation and sonographer performance. Abdominal Radiology 2026 View