Artificial intelligence and machine learning in cancer imaging
Dow‐Mu Koh, Nickolas Papanikolaou, Ulrich Bick, Rowland Illing, Charles E. Kahn, Jayshree Kalpathi-Cramer, Celso Matos, Luis Martí‐Bonmatí, Anne Miles, Seong K. Mun, Sandy Napel, Andrea Rockall, Evis Sala, Nicola H. Strickland, Fred Prior
Abstract
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.