National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation

Purpose: Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and 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. Methods and Materials: The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 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. Results: 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) accelerating learning and funding opportunities. Conclusion: Together, these action points can facilitate the translation of AI into clinical practice. (C) 2020 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

Automated summary

The article discusses the importance of training radiation oncologists and medical physicists in data science to effectively utilize AI in clinical practice. Action points for future trainees interested in radiation oncology AI include raising awareness, implementing practical curriculum, creating a database of resources, and accelerating learning and funding opportunities to facilitate the translation of AI into clinical practice.