Current state of mammography-based artificial intelligence for future breast cancer risk prediction: a systematic review
Future studies should evaluate models using digital breast tomosynthesis, examine performance for aggressive or advanced breast cancer, include diverse populations, and evaluate both discrimination and model calibration. Prospective evaluations are needed to determine the clinical utility of mammography-based AI models for personalized risk-based breast cancer screening before implementation.
Authors
- Kevin S. Hughes (ORCID: https://orcid.org/0000-0003-4084-6484)
- CM Vachon
- Ki Hwan Kim (ORCID: https://orcid.org/0000-0001-7684-235X)
- Han Eol Jeong
- Adam Yala
- Kathryn P. Lowry
- Karla Kerlikowske
Institutions
- Pacific Northwest Diabetes Research Institute (US)
- University of Wisconsin–Madison (US)
- Medical University of South Carolina (US)
- University of California, San Francisco (US)
- University of Washington (US)
- Luther University (KR)
- University of California System (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
- Wisconsin Division of Public Health (US)
- University of California, Berkeley (US)
Publication Details
- Journal
- JNCI Journal of the National Cancer Institute
- Published
- 2026-01-06
- DOI
- https://doi.org/10.1093/jnci/djag002
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 104.19