A Learning Accelerator Framework: Scalable Clinical AI Development and Delivery.
Authors
Affiliations (4)
Affiliations (4)
- Data-driven Strategies for Medicine & Biotechnology, Mercer Island, WA. Electronic address: [email protected].
- DeepHealth Inc., Somerville, MA.
- Department of Radiology, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI.
- DeepHealth Inc., Somerville, MA; RadNet Inc, Los Angeles, CA.
Abstract
To introduce a vertically integrated model between a healthcare service provider and technology developer as a learning accelerator to address challenges in developing and delivering artificial intelligence (AI) into healthcare. The Learning Accelerator Framework is built on four core components that focus on improving patient and healthcare outcomes: an integrated data registry, a continuous technology development stack, adaptive clinical services, and an iterative learning and development loop. Its application is described in one case study to highlight its operational mechanisms throughout the AI lifecycle. The Framework has guided the conceptualization, development, implementation and national delivery of a multi-stage AI breast cancer screening workflow, progressing from initial clinical validation on thousands to millions of patients. We demonstrate how iterative learning loops were applied using real-world clinical and monitoring feedback, which resulted in a multi-stage AI screening workflow that has achieved a significant absolute increase in cancer detection rate (Δ0.99 cancers/1000 exams [95% confidence interval: 0.59-1.42]) and positive predictive value (Δ0.55 cancers/100 recalls [95% confidence interval: 0.30-1.03) with equitable benefits across breast density, race, and ethnic subpopulations. 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.