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T he radiology community has had a leading role in exploring medical applications of artificial intelligence (AI), and one of the primary drivers for this is the desire for increased accuracy and efficiency in clinical care. Radiologist responsibilities extend beyond image interpretation. AI tools have the potential to improve essential tasks in the imaging value chain, from image acquisition to generating and disseminating radiology reports (1). These applications are crucial in current medical environments with increasing workloads, increasing scan complexity, and the need to decrease costs and reduce errors (2-4). AI applications related to radiologic quality, safety, and workflow improvements can be grouped by their influence on various steps in the typical radiology workflow, as follows in their approximate order of occurrence: study selection and protocoling; image acquisition; worklist prioritization; study reporting, business applications, and resident education. This qualitative review is a discussion of current research and commercial models regarding these applications within the entire imaging chain. Methods Studiespublished from 1980 through 2019 were retrieved nonsystematically from academic search engines including PubMed, ScienceDirect, and Google Scholar by using search terms related to each application of interest. Public legal documents were also accessed including the Medicare Physician Fee Schedule and Other Revisions to Part B, Quality Payment Program requirements, and Shared Savings Program requirements.Public news sources, such as Becker's Hospital Review, Healthcare Finance, Optum, and Healthcare IT News, and vendor lists from meetings of the Radiological Society of North America and the Society for Imaging Informatics in Medicine were used to find any commercial efforts in each space. All searches were performed by the authors, all of whom are attending radiologists or trainees with a research interest in radiology AI.

It is well known that patients who suffer from peripheral (noncardiac) vascular disease often have coexisting atherosclerotic diseases of the heart. This may leave the patients susceptible to major adverse cardiac events, including death, myocardial infarction, unstable angina, and pulmonary edema, during the perioperative time period, in addition to the many other complications they may sustain as they undergo vascular surgery procedures, regardless of whether the procedure is performed as an open or endovascular modality. As these patients are at particularly high risk, up to 16% in published studies, for postoperative cardiac complications, many proposals and algorithms for perioperative optimization have been suggested and studied in the literature. Moreover, in patients with recent coronary stents, the risk of non-cardiac surgery on adverse cardiac events is incremental in the first 6 months following stent implantation. Just as postoperative management of patients is vital to the outcome of a patient, preoperative assessment and optimization may reduce, and possibly completely alleviate, the risks of major postoperative complications, as well as assist in the decision-making process regarding the appropriate surgical and anesthetic management. This review article addresses several tools and therapies that treating physicians may employ to medically optimize a patient before they undergo noncardiac vascular surgery.