A Learning Accelerator Framework: Scalable Clinical Artificial Intelligence Development and Delivery
The Learning Accelerator Framework represents a departure from traditional approaches by mitigating challenges, inefficiencies, and delays that impede AI translation, offering a model for AI developers and provider systems seeking to accelerate innovation. The breast AI case study demonstrates how instrumental the framework can be for ensuring ongoing AI implementation effectiveness, fostering clinician trust, and ultimately improving operations, patient outcomes and health equity.
Authors
- Christoph I. Lee (ORCID: https://orcid.org/0000-0002-8185-7721)
- A. Gregory Sorensen (ORCID: https://orcid.org/0000-0001-7750-8206)
- Edgar A. Wakelin (ORCID: https://orcid.org/0000-0001-5433-5824)
- Bryan Haslam (ORCID: https://orcid.org/0009-0005-1546-7433)
- Diana S.M. Buist (ORCID: https://orcid.org/0000-0001-5408-2804)
- Annie Ng (ORCID: https://orcid.org/0000-0002-5975-2499)
- Sham Sokka
Institutions
- Bio-Rad (United States) (US)
- University of Wisconsin–Madison (US)
- Somerville Hospital (US)
- Mercer Island School District (US)
- Wisconsin Division of Public Health (US)
Publication Details
- Journal
- Journal of the American College of Radiology
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.jacr.2025.12.015
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00