2022 ASM / R-0203
Insights from implementation of an artificial intelligence assist device across a national radiology network
Type:
Educational Exhibit
Keywords:
Artificial Intelligence, Conventional radiography, Technology assessment, Quality assurance
Authors:
S. Karunasena, M. Vasimalla, C. Jones
DOI:
10.26044/ranzcr2022/R-0203
References
- Thrall JH, Li X, Li Q, Cruz C, Do S, Dreyer K, Brink J. Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success. Journal of the American College of Radiology. 2018 Mar 1;15(3):504-8.
- Aggarwal R, Sounderajah V, Martin G, Ting DS, Karthikesalingam A, King D, Ashrafian H, Darzi A. Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis. NPJ digital medicine. 2021 Apr 7;4(1):1-23.
- Huisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, Pinto de Santos D, Coppola F, Morozov S, Zins M, Bohyn C, Koç U. An international survey on AI in radiology in 1,041 radiologists and radiology residents part 1: fear of replacement, knowledge, and attitude. European radiology. 2021 Sep;31(9):7058-66.
- Seah JC, Tang CH, Buchlak QD, Holt XG, Wardman JB, Aimoldin A, Esmaili N, Ahmad H, Pham H, Lambert JF, Hachey B. Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. The Lancet Digital Health. 2021 Aug 1;3(8):e496-506.