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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 shephe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Radiation Oncology. Published by Elsevier Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293478/ https://www.ncbi.nlm.nih.gov/pubmed/32544635 http://dx.doi.org/10.1016/j.prro.2020.06.001 |
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author | Kang, John Thompson, Reid F. Aneja, Sanjay Lehman, Constance Trister, Andrew Zou, James Obcemea, Ceferino El Naqa, Issam |
author_facet | Kang, John Thompson, Reid F. Aneja, Sanjay Lehman, Constance Trister, Andrew Zou, James Obcemea, Ceferino El Naqa, Issam |
author_sort | Kang, John |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7293478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | American Society for Radiation Oncology. Published by Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-72934782020-06-14 National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation Kang, John Thompson, Reid F. Aneja, Sanjay Lehman, Constance Trister, Andrew Zou, James Obcemea, Ceferino El Naqa, Issam Pract Radiat Oncol Special Article 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. American Society for Radiation Oncology. Published by Elsevier Inc. 2021 2020-06-13 /pmc/articles/PMC7293478/ /pubmed/32544635 http://dx.doi.org/10.1016/j.prro.2020.06.001 Text en © 2020 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Special Article Kang, John Thompson, Reid F. Aneja, Sanjay Lehman, Constance Trister, Andrew Zou, James Obcemea, Ceferino El Naqa, Issam National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation |
title | National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation |
title_full | National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation |
title_fullStr | National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation |
title_full_unstemmed | National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation |
title_short | National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation |
title_sort | national cancer institute workshop on artificial intelligence in radiation oncology: training the next generation |
topic | Special Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293478/ https://www.ncbi.nlm.nih.gov/pubmed/32544635 http://dx.doi.org/10.1016/j.prro.2020.06.001 |
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