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