ArticleRadiation oncology (London, England)2024
A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023.
Article in Radiation oncology (London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis 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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global trends in research of neuroimmune in lung diseases over past decade: a bibliometric analysis.Frontiers in immunology · 2026Pooled it
- Mapping patients' and professionals' perceptions of artificial intelligence in radiotherapy: a scoping review.Technical innovations & patient support in radiation oncology · 2026Review
- The Cerebellar Role in Emotions at a Turning Point: Bibliometric Analysis and Collaboration Networks.Cerebellum (London, England) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
backgroundRecent research has demonstrated that the use of artificial intelligence (AI) in radiotherapy (RT) has significantly streamlined the process for physicians to treat patients with tumors; however, bibliometric studies examining the correlation between AI and RT are not available. Providing a thorough overview of the knowledge structure and research hotspots between AI and RT was the main goal of the current study.
methodA search was conducted on the Web of Science Core Collection (WoSCC) database for publications pertaining to AI and RT between 2003 and 2023. VOSviewers, CiteSpace, and the R program "bibliometrix" were used to do the bibliometric analysis.
resultsThe analysis comprised 615 publications from 64 countries, with USA and China leading the pack. Since 2017, there have been more and more publications about RT and AI every year. The research center that made the biggest contribution to this topic was Maastricht University. The most articles published journal in this field was Frontiers in Oncology, while Medical Physics received the greatest number of citations. Dekker Andre is the author with the greatest number of published articles, while Philippe Lambin was the most often co-cited author. In the newly identified research hotspots, "autocontouring algorithm", "deep learning", and "machine learning" stand out as the main terms.
conclusionIn fact, our bibliometric analysis offers insightful information on current research directions and advancements pertaining to the use of AI in RT. For academics looking to understand the connection between AI and RT, this study is a great resource because it highlights current research frontiers and hot trends.
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.