{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lv M"],"funding":["Science and technology development project of Shanghai University of Traditional Chinese Medicine","the Integrated Chinese and western medicine project of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine","the Shanghai Health Commission"],"pagination":["157"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11552138"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["19(1)"],"pubmed_abstract":["<h4>Background</h4>Recent 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.<h4>Method</h4>A 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.<h4>Results</h4>The analysis comprised 615 publications from 64 countries, with USA and China leading the pack"],"journal":["Radiation oncology (London, England)"],"pubmed_title":["A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023."],"pmcid":["PMC11552138"],"funding_grant_id":["23KFL105","SGZXY-202201","202340160"],"pubmed_authors":["Zeng S","Lv M","Guan W","Zhao R","Zhang Y","Feng Y","Zeng H","E X","Yu J","Shen W"],"additional_accession":[]},"is_claimable":false,"name":"A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023.","description":"<h4>Background</h4>Recent 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.<h4>Method</h4>A 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.<h4>Results</h4>The analysis comprised 615 publications from 64 countries, with USA and China leading the pack","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2025-04-04T00:32:38.169Z","creation":"2025-04-04T00:32:38.169Z"},"accession":"S-EPMC11552138","cross_references":{"pubmed":["39529129"],"doi":["10.1186/s13014-024-02551-1"]}}