Will Robots Run Radiology?

by Kori Stewart, PhD, R.T.(R)(CT)(ARRT), CIIP on Jun 28, 2018

Radiology_Predictions-blogIf you follow the latest trends and emerging technologies in healthcare, you have surely seen endless headlines about artificial intelligence (AI) in radiology. The buzz is well within reason. Artificial intelligence encompasses an extensive range of computerized technologies to solve problems in ways that imitate human thinking.

Machine learning, a subset of AI and form of clinical data science, provides an opportunity to improve the quality of care for patients and is an exceptional opportunity for radiology departments to remain on the forefront in informatics. Machine learning can be viewed as a toolbox of mysterious, but important, mathematical techniques (or algorithms) that empower computers to improve task performance.

So, does this mean machines or robots will be running radiology departments soon? No, but this form of artificial intelligence will play a significant role in the future of imaging and value-based care.

Medical imaging data has grown exponentially, with the potential to transform how we care for patients. Radiology images are intriguing, and image interpretation allures informatics professionals with expertise in machine learning. Algorithms have improved, and technology is advancing. Most of us can remember the days of film and would agree that radiology departments have quickly transformed into digital environments. AI will soon generate compelling insight into the data we acquire, and radiology departments will work alongside these powerful industry techniques.

Machine learning can provide powerful insight when used in image interpretation, clinical decision support, measuring radiology patient outcomes, data integration and knowledge management. Machine learning algorithms can provide enhanced efficiency and accuracy in interpretation of images as well as facilitate follow-up management of reported findings.

Current uses of machine learning include computer-aided detection and diagnosis (CAD), content-based image retrieval, automated classification of radiology reports, prediction of overall survival of cancer patients using “radiomics” and prediction of transition from mild cognitive impairment to Alzheimer’s disease.

It is imperative that radiologic technologists understand the vocabulary of informatics and its emerging technologies, such as machine learning. In a recent article in the Journal of the American College of Radiology, Kruskal, et al. stated that “understanding imaging informatics is as central to radiology education as radiation biology and MRI physics.” Radiologists and technologists are going to be working alongside AI as we continue to pioneer informatics applications in our field, and comprehension is key to successful implementation. The real value in machine learning lies in the potential for deeper knowledge of the data acquired and future of value-based patient care. The possibilities are endless with informatics!

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Meet the Author

Dr. Stewart has been a dedicated educator and technologist since 2009 and holds certifications in general radiography, computed tomography and imaging informatics. After spending 12+ years in higher education serving as a faculty member, Clinical Director and Program Director of Radiologic Technology, she transitioned to her current role with the Society for Imaging Informatics in Medicine (SIIM) as Director of Training. Dr. Stewart is also serving as an Adjunct Professor at Quinnipiac University in the Master of Science, Radiologist Assistant Program. Dr. Stewart is full co-author of the 8th Edition of “Introduction to Radiologic & Imaging Sciences & Patient Care”, as well an invited full co-author of the upcoming 7th Edition of “Principles of Radiographic Imaging: An Art and Science”. Both of these textbooks are used by Radiologic and Imaging Science programs nationally and internationally. Dr. Stewart is the sole author of an Informatics in Medical Imaging chapter in the 6th Edition of the textbook “Principles of Radiographic Imaging: An Art and Science” and has published numerous peer-reviewed articles. She has presented nationally on a variety of medical imaging topics with a focus on imaging informatics and patient care, and continues to be passionate about sharing her love of lifelong learning. She is an active member of the American Society of Radiologic Technologists (ASRT) and the Connecticut Society of Radiologic Technologists (CSRT). She is also a member of the Association of Educators in Imaging and Radiologic Sciences (AEIRS) and is currently serving on the Board of Directors as Secretary/Treasurer. Dr. Stewart is also a member of the Specialty Task Force for the American Board of Imaging Informatics (ABII) and has been an active member of the Society for Imaging Informatics in Medicine (SIIM) since 2016, having served on the Education Committee, Resident/Fellow/Doctoral Students (RFDS) Committee, and Imaging Informatics Professional Internship Subcommittee.

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