Artificial intelligence in radiotherapy
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
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AI is used at all steps of radiotherapy, including image segmentation, treatment planning, outcome prediction, quality assurance, and AI-guided radiosurgery.
“This includes the use of AI at all steps of radiotherapy, including image segmentation, treatment planning, outcome prediction, quality assurance, and AIguided radiosurgery (116).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [116]
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References 186 · Cited by 46
- Authors
- Li, G., Wu, X., & Ma, X.
- Affiliations
- Sichuan University (CN)
- ORCID
- Venue
- Seminars in Cancer Biology
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 86
- Issue
- Pt 2
- Pages
- 160-171
- ISSN
- 1044-579X (ISSN-L)
- OpenAlex
- W4292552144
- Cited by
- 46
- References
- 186
- Citations by year
- 2026: 9 · 2025: 10 · 2024: 14 · 2023: 13
- FWCI
- 1.54
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Seminars in Cancer Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence, radiotherapy, radiation oncology, medical image processing, contouring, radiotherapy treatment planning, quality assurance, motion management, outcome prediction, multi-omics, workflow automation, biobanks
- MeSH terms
- Artificial Intelligence, Humans, Radiotherapy Planning, Computer-Assisted, Radiation Oncology
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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