{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T12:04:16Z","timestamp":1791893056806,"version":"4.3.4"},"reference-count":105,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This review provides an overview of the application of artificial intelligence (AI) in radiation therapy (RT) from a radiation oncologist\u2019s perspective. Over the years, advances in diagnostic imaging have significantly improved the efficiency and effectiveness of radiotherapy. The introduction of AI has further optimized the segmentation of tumors and organs at risk, thereby saving considerable time for radiation oncologists. AI has also been utilized in treatment planning and optimization, reducing the planning time from several days to minutes or even seconds. Knowledge-based treatment planning and deep learning techniques have been employed to produce treatment plans comparable to those generated by humans. Additionally, AI has potential applications in quality control and assurance of treatment plans, optimization of image-guided RT and monitoring of mobile tumors during treatment. Prognostic evaluation and prediction using AI have been increasingly explored, with radiomics being a prominent area of research. The future of AI in radiation oncology offers the potential to establish treatment standardization by minimizing inter-observer differences in segmentation and improving dose adequacy evaluation. RT standardization through AI may have global implications, providing world-standard treatment even in resource-limited settings. However, there are challenges in accumulating big data, including patient background information and correlating treatment plans with disease outcomes. Although challenges remain, ongoing research and the integration of AI technology hold promise for further advancements in radiation oncology.<\/jats:p>","DOI":"10.1093\/jrr\/rrad090","type":"journal-article","created":{"date-parts":[[2023,10,19]],"date-time":"2023-10-19T01:19:48Z","timestamp":1697678388000},"page":"1-9","source":"Crossref","is-referenced-by-count":89,"title":["Revolutionizing radiation therapy: the role of AI in clinical practice"],"prefix":"10.1093","volume":"65","author":[{"given":"Mariko","family":"Kawamura","sequence":"first","affiliation":[{"name":"Nagoya University Graduate School of Medicine Department of Radiology, , 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takeshi","family":"Kamomae","sequence":"additional","affiliation":[{"name":"Nagoya University Graduate School of Medicine Department of Radiology, , 65 Tsurumaicho, Showa-ku, Nagoya, Aichi, 466-8550, 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