article<\/a> in the journal Radiology, BU researchers provide an overview of AI applications for musculoskeletal radiology, including basic principles, image acquisition and interpretation, and prediction of future outcomes. Their article also discusses AI implementation challenges, the non-interpretive uses of AI and how it may transform the daily professional lives of musculoskeletal radiologists.<\/p>\nAccording to the researchers, AI shows great potential for more complex tasks such as disease prognostication and prediction of clinical outcomes over time, which may increase the value of imaging and allow the field to take a big step forward toward precision medicine.<\/p>\n
However, many challenges need to be overcome for AI to make its way to clinical practice. These include the requirement for large, good-quality data sets, which is more problematic for uncommon conditions such as musculoskeletal tumors, among others. They point out that multi-institutional collaboration will be essential to the creation of such data sets, but this introduces issues of its own such as differences in imaging protocols.<\/p>\n
\u201cFor AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will require close collaboration between core AI researchers and clinical radiologists,\u201d says Guermazi.<\/p>\n
Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist\u2019s workflow. Additional AI applications also may be helpful for business, education and research purposes if successfully integrated into the daily practice of musculoskeletal radiology.<\/p>\n
Guermazi reassures that AI will not replace radiologists, but rather radiologists in the future will all use AI.<\/p>\n
<\/p>\n
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AI-based tools can improve the musculoskeletal radiologist\u2019s workflow by triaging imaging examinations, helping with image interpretation and decreasing the reporting time.<\/p>\n","protected":false},"author":903,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[90],"tags":[],"_links":{"self":[{"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/posts\/118649"}],"collection":[{"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/users\/903"}],"replies":[{"embeddable":true,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/comments?post=118649"}],"version-history":[{"count":2,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/posts\/118649\/revisions"}],"predecessor-version":[{"id":118651,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/posts\/118649\/revisions\/118651"}],"wp:attachment":[{"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/media?parent=118649"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/categories?post=118649"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.bumc.bu.edu\/camed\/wp-json\/wp\/v2\/tags?post=118649"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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\u201cWith the ongoing trend of increased imaging rates and decreased acquisition times, a variety of AI tools can support musculoskeletal radiologists by providing more optimized and efficient workflows,\u201d says corresponding author Ali Guermazi<\/a>, MD, PhD, chief of radiology at VA Boston Healthcare System and professor of radiology and medicine at Boston University Chobanian & Avedisian School of Medicine.<\/p>\n