A Comprehensive AI-Based Approach in Classifying Breast Lesions: Focusing on Improving Pathologists’ Accuracy and Efficiency
This study highlights the potential of AI in breast lesion classification, demonstrating high sensitivity, specificity, and efficiency, and supports its integration into routine pathology practice.
Authors
- Zaibo Li (ORCID: https://orcid.org/0000-0003-1325-1696)
- David A. Kellough
- Giovanni Lujan (ORCID: https://orcid.org/0000-0003-1392-6155)
- Manuela Vecsler (ORCID: https://orcid.org/0000-0002-7951-4885)
- Wendy L. Frankel (ORCID: https://orcid.org/0000-0003-1658-1009)
- Nada Shaker (ORCID: https://orcid.org/0000-0001-6145-256X)
- Anil Vasdev Parwani (ORCID: https://orcid.org/0000-0002-7616-1666)
- Shaya Goodman
- Yan Hu (ORCID: https://orcid.org/0000-0003-2285-2282)
- Himani Kumar (ORCID: https://orcid.org/0009-0007-6035-1135)
- Maryam Tahir (ORCID: https://orcid.org/0009-0009-4355-2466)
Institutions
- The Ohio State University Wexner Medical Center (US)
Publication Details
- Journal
- Clinical Breast Cancer
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1016/j.clbc.2025.03.016
- Citations
- 5
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 12.40