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Imaging Informatics (450) Clinical Information and Decision Making (3590) Decision Support for Health Professionals (2160) Machine Learning (3089) Breast Cancer (402) Generative Language Models Including ChatGPT (1445)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70924, first published .
Medical professional holding an ultrasound transducer near an ultrasound machine.

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

Authors of this article:

Su Miaojiao1 Author Orcid Image ;   Liang Xia1 Author Orcid Image ;   Zeng Xian Tao1 Author Orcid Image ;   Hong Zhi Liang1 Author Orcid Image ;   Cheng Sheng1 Author Orcid Image ;   Wu Songsong1 Author Orcid Image
Article Authors Cited by (11) Tweetations Metrics

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;33(7):2869 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
  8. Yıldırım S, Ülker E, Poyraz N. Natural Language Processing in Breast Imaging Reports: A Scoping Review with Implications for Low-Resource Clinical Languages. Applied Sciences 2026;16(12):5847 View
  9. Cîrlig I, Olaru A, Ene M, Domenco A, Teică R, Ciofiac C, Nica R, Novac V, Mămuleanu M, Florescu L, Gheonea I. The Impact of Artificial Intelligence on BI-RADS Classification and Diagnostic Confidence in Mammography Interpretation by Radiology Residents. Life 2026;16(6):1020 View
  10. Khan A, Shaheen B, Ahmed M. Using Machine Learning and LLM for Classifying of Benign and Malignant Cells from Breast Cancer Dataset. International Journal of Innovations in Science and Technology 2026:190 View
  11. He P, Chen C, Dong H, Chen M, Lin Z, Wu S, Cai W, Lin X, Dong Y, Wang C, Fu F. Vision Language Models for Ultrasound Assessment of Suspicious Axillary Lymph Nodes in Breast Cancer. American Journal of Roentgenology 2026 View