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arXiv · 1910.13334

NCI Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation

John Kang·Reid F. Thompson·Sanjay Aneja·Constance Lehman·Andrew Trister·James Zou·Ceferino Obcemea·Issam El Naqa
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Abstract

Artificial intelligence (AI) is about to touch every aspect of radiotherapy from consultation, treatment planning, quality assurance, therapy delivery, to outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area. The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first (https://dctd.cancer.gov/NewsEvents/20190523_ai_in_radiation_oncology.htm) of two data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI. In this perspective article written by members of the Training and Education Working Group, we provide and discuss Action Points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerate learning and funding opportunities. Together, these Action Points can facilitate the translation of AI into clinical practice.

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John Kang, Reid F. Thompson, Sanjay Aneja, Constance Lehman, Andrew Trister, James Zou, Ceferino Obcemea, Issam El Naqa. 2020-07-05. NCI Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation. https://doi.org/10.1016/j.prro.2020.06.001

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