Review of Artificial Intelligence Business Cases to Advance Toward Learning Health Care Systems

Multiple barriers have been identified to developing a learning health care systems (LHSs) including organizational culture, data systems and interoperability, funding and workforce limitations, and regulatory challenges. Artificial intelligence (AI) is being explored both inside and outside of health care, with varying degrees of scientific rigor in the testing of AI applications. LHSs and AI face similar implementation challenges, which presents an opportunity for synergy. By reviewing AI use cases from the lens of how it can be used to reduce previously identified barriers to progressing toward an LHS, opportunities for facilitating this journey can be identified. AI tools can impact both clinical and nonclinical business processes. The process of testing and implementing AI tools based on high-quality evidence or signal should prespecify thresholds and expectations of incremental effectiveness (marginal risk-benefit) improvement compared with current standards of care, as is standard in health services research, quality improvement, process improvement, and best practices of comparative health care research. Business process examples to improve workflow using AI tools may adhere to less rigorous evidentiary standards compared with tools guiding patient-centered clinical decision scenarios, such as with AI-based diagnostic applications. This review indicates that AI tools provide tremendous opportunities for radiology to improve health care systems, workflow processes, and patients' health outcomes.