Artificial intelligence tools will help enhance the practice of musculoskeletal radiology | va boston health care | veterans affairs

BOSTON , MA — Boston University, VA Boston and other researchers said AI-based tools can assist musculoskeletal radiologists by triaging imaging examinations, helping with interpretation and

decreasing reporting time, according to commentary published in the journal Radiology Jan. 2, 2024. “With 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,” said corresponding author Dr. Ali Guermazi, chief of radiology at VA Boston

Healthcare System and professor of radiology and medicine at BU Chobanian & Avedisian School of Medicine. In the article, researchers provided an overview of AI applications for

musculoskeletal radiology, including basic principles, image acquisition and interpretation, and prediction of future outcomes. The article also discusses AI implementation challenges, the

non-interpretive uses of AI and how it may transform the daily professional lives of musculoskeletal radiologists.  According to the researchers, AI shows great potential for more complex

tasks, as well, 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.  However, the researchers cautioned that AI has many challenges to be overcome before making its way into 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 pointed out that multi-institutional collaboration will be

essential to the creation of such data sets, but that this introduces issues of its own, such as differences in imaging protocols. “For 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,” said Guermazi. He added that AI will not replace radiologists, but will enhance their practice. The researchers added that additional AI applications

may also be helpful for business, education and research purposes, if successfully integrated into the daily practice of musculoskeletal radiology.  The article can be found at

https://pubs.rsna.org/doi/10.1148/radiol.230764