Artificial intelligence will reduce the need for clinical medical physicists

摘要

In 2011, IBM's supercomputer Watson defeated the former human winners and won the first prize on Jeopardy! game. It has created an overly publicized attention on machine learning and Artificial Intelligence (AI). Early this year, Google AlphaGo has marked a major breakthrough in AI by winning the first game against the world's best champion human player in the world's most complex game, the ancient Chinese Go game. With no doubt, the interests in AI and its related products had reached a global frenzy. As scientists advance in technology, a concern of job security has risen up: will robots take our jobs? IBM Watson has evolved from a “question answering machine” to a highly intelligent “cognitive diagnostic engine” or a “decision support system” over the past 6 yr. Based on Carl Frey and his collaborators, future family health centers may transition to a team of nurse practitioners with the support of Watson Health and overseen by one single doctor.1 Will AI technology also marginalize medical physicists in the near future? In this series, we have Dr. Xiaoli Tang arguing for the proposition that “AI will reduce the need for clinical medical physicists” and Dr. Brian Wang arguing against it. Dr. Xiaoli Tang received a Ph.D in Electrical Engineering from the Rensselaer Polytechnic Institute. She then did her postdoctoral training in Medical Physics at the Massachusetts General Hospital and the University of California at San Diego. She previously worked at the University of North Carolina and now is working as an Assistant Attending and chief physicist at the Memorial Sloan Kettering Cancer Center Westchester regional site. She is an expert in motion management, Deep Inspiration Breath Hold (DIBH) for left-sided breast cancer, and machine learning algorithms on medical physic applications. She is interested in developing related clinical trials, and bringing new technology to the clinic. She is a member of the American Association of Physicists in Medicine (AAPM), and the American Society for Radiation Oncology. Dr. Brian Wang received his PhD in nuclear engineering from Rensselaer Polytechnic Institute in Troy, NY in 2005. He currently works at University of Louisville as the chief of physics and medical physics residency director. Dr. Wang is an associate editor for the JACMP. His research interests include motion management, image guidance, and SRS/SBRT. Dr. Wang has been involved with the AAPM Spring Clinical Meeting and its predecessor ACMP annual meeting as a program director or the subcommittee chair for 8 yr. Dr. Wang serves on several committees at ASTRO, RSS, and ABR. Technology always puts existing jobs under strain. Without doubt, the next technological evolution is the Artificial Intelligence (AI). Already, there is an estimate of 20–40 M jobs in peril in the US from developments in AI and its related technology, which counts 15%–30% of the US labor force.2 This is happening in clinical Medical Physics as well. Out of many duties that medical physicists have taken upon, the clinical aspect mainly includes treatment planning, chart checking, and machine quality assurance (QA). The need of clinical physicists in these areas has already been slowly reduced over the past several years, and this speed is going to be increased as more AI technologies are implemented in the clinic. Let us look at therapeutic physics first. The need for physicists on planning has largely been shifted to dosimetrists. With the AI-fueled automatic planning software, the need for physicists will be further reduced or eventually eliminated. Many articles have been published on knowledge-based planning.3-6 It is a novel treatment planning technique capable of estimating the dose volume histograms (DVHs) of organs at risk based on the DVHs of previous plans with similar characteristics. Vendors have implemented this to their new automated or semi-automated treatment systems. Varian's (Varian Medical Systems Inc, Palo Alto, CA, USA) RapidPlan allo