A Learning Accelerator Framework: Scalable Clinical Artificial Intelligence Development and Delivery

Abstract

OBJECTIVES: To introduce a vertically integrated model between a health care service provider and technology developer as a learning accelerator to address challenges in developing and delivering artificial intelligence (AI) into health care. METHODS: The Learning Accelerator Framework is built on four core components that focus on improving patient and health care 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 life cycle. RESULTS: The framework has guided the conceptualization, development, implementation, and national delivery of a multistage AI breast cancer screening workflow, progressing from initial clinical validation (thousands) to population-scale implementation (millions of patients). We demonstrate how iterative learning loops were applied using clinical feedback and real-world data monitoring feedback, which resulted in a multistage AI screening workflow that has achieved a significant absolute increase in cancer detection rate (Δ0.99 cancers per 1,000 examinations [95% confidence interval: 0.59-1.42]) and positive predictive value (Δ0.55 cancers per 100 recalls [95% confidence interval: 0.30-1.03]) with equitable benefits across breast density, race, and ethnic subpopulations. DISCUSSION: 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.

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