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Overview
Recently there has been a significant surge in applications of artificial intelligence (AI) in medicine, suggesting that AI may soon dramatically change the landscape of health care and thus medical physics. It is generally expected that AI will improve overall health care in an enormous way. Some believe that AI will similarly impact medical physics research and practice, while others believe that these expectations to be unrealistic due to issues of technical validation and practical limitations of AI. This is the premise debated in this month's Point/Counterpoint.
 

Arguing for the Proposition is Lei Xing, Ph.D. Dr. Xing is currently the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. Dr. Xing also holds affiliate faculty positions in Department of Electrical Engineering, bioinformatics, Bio‐X, and Molecular Imaging Program at Stanford. Dr. Xing's research has been focused on medical imaging, treatment planning, AI in medicine, image‐guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing has made unique and significant contributions to each of the above areas. Dr. Xing is an author on more than 300 peer reviewed publications, a co‐inventor on many issued and pending patents, and a principal investigator or co‐investigator on numerous NIH, DOD, ACS, RSNA, and corporate grants. Dr. Xing is a fellow of AAPM (American Association of Physicists in Medicine) and AIMBE (American Institute for Medical and Biological Engineering).

Arguing against the Proposition is Elizabeth A. Krupinski, Ph.D. Dr. Krupinski joined Emory University in 2015 and is the Vice Chair for Research in the Department of Radiology and Imaging Sciences. Prior to that, she has worked at the University of Arizona for 23 yr. Dr. Krupinski is an Experimental Psychologist with research interests in medical image perception, observer performance, medical decision‐making, and human factors as they pertain to radiology and telemedicine. Her research aims to improve our understanding of the perceptual and cognitive mechanisms underlying the interpretation of medical images in order to reduce errors, improve training, and optimize the reading environment, thereby improving patient care and outcomes.

Author information

Xing L1, Krupinski EA2, Cai J.
1
Department of Radiation Oncology, Stanford University, Stanford, CA, 94305, USA.
2
Department of Radiology & Imaging Sciences, Emory University, Atlanta, GA, 30322, USA.