Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology

Abstract

Artificial intelligence (AI) and machine learning (ML) are increasingly omnipresent in modern life and becoming integrated into health care. Radiology studies are large data sets in which megabytes of image data are typically distilled into a short text-based data set (ie, the radiologist's report) highlighting clinically relevant information (pathology or other findings termed biomarkers). AI and ML applications are highly suited to aid in this process of synthesizing key elements from raw data. Potential benefits include improved diagnostic accuracy, enhanced efficiency, new biomarker discovery, optimized post-treatment diagnosis of complications, and potentially less costly health care. Automated radiologic image diagnosis is forecast to save USD $3 billion annually in the United States “by giving radiologists more time to focus on reviews that require greater interpretation or judgment” [ 1 x [1] Kalis, B., Collier, M., and Fu, R. 10 promising AI applications in health care. Harvard Business Review. 2018; (Available at: https://hbr.org/2018/05/10-promising-ai-applications-in-health-care. Accessed January 14, 2019) Google Scholar See all References ] [1] .