ArticlePractical radiation oncology
National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation.
Article in Practical radiation oncology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness.Annals of medicine · 2024Pooled it
- A Systematic Review on Healthcare Artificial Intelligent Conversational Agents for Chronic Conditions.Sensors (Basel, Switzerland) · 2022Pooled it
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Article
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection.Journal of imaging informatics in medicine · 2024Article
- Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.Journal of applied clinical medical physics · 2024Article
- Radiologic Technology Students' Perceptions on Adoption of Artificial Intelligence Technology in Radiology.International journal of general medicine · 2024Article
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- Review
- Artificial intelligence in radiology: Are Saudi residents ready, prepared, and knowledgeable?Saudi medical journal · 2022Observational
- Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review.JMIR medical education · 2021Article
- Accelerating the Appropriate Adoption of Artificial Intelligence in Health Care: Protocol for a Multistepped Approach.JMIR research protocols · 2021Article
- A roadmap for research in medical physics via academic medical centers: The DIVERT Model.Medical physics · 2021Article
- Provider Engagement in Radiation Oncology Data Science: Workshop Report.JCO clinical cancer informatics · 2020Article
- Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes.Journal of medical education and curricular developmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
purposeArtificial 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.
resultsIn 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.
conclusionTogether, these action points can facilitate the translation of AI into clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.