The capabilities of and interest in artificial intelligence (AI) in healthcare, and more specifically, population health, has grown exponentially over the past decade. The vast volume of digital data or “big data” in the form of images generated by an aging population, with an ever-increasing demand for imaging, amassed by radiology departments, provides ample opportunity for AI application and has allowed radiology to become a service line leader of AI in the medical field. The screening and detection capabilities of AI make it a valuable tool in population health management, as organizations work to shift their services to early identification and intervention, especially as it relates to chronic disease. In this paper, the clinical, technological, and operational workflows that were developed and integrated within each other to support the adoption of AI algorithms aimed at detecting subclinical osteoporosis and coronary artery disease are described. The benefits of AI are reviewed and weighed against potential drawbacks within the context of population health management and risk contract arrangements. Mitigation tactics are discussed, as well as the anticipated outcomes in terms of cost-avoidance, physician use of evidence-based clinical pathways, and reduction in major patient events (e.g., stroke, hip fracture). The plan for data collection and analysis is also described for program evaluation.
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