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Eng."],"published-print":{"date-parts":[[2025,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Artificial intelligence (AI) incorporation into healthcare has proven revolutionary, especially in radiotherapy, where accuracy is critical. The purpose of the study is to present patterns and develop topics in the application of AI to improve the precision of anatomical diagnosis, delineation of organs, and therapeutic effectiveness in radiation and radiological imaging. We performed a bibliometric analysis of scholarly articles in the fields starting in 2014. Through an examination of research output from key contributing nations and institutions, an analysis of notable research subjects, and an investigation of trends in scientific terminology pertaining to AI in radiology and radiotherapy. Furthermore, we examined software solutions based on AI in these domains, with a specific emphasis on extracting anatomical features and recognizing organs for the purpose of treatment planning. Our investigation found a significant surge in papers pertaining to AI in the fields since 2014. Institutions such as Emory University and Memorial Sloan\u2013Kettering Cancer Center made substantial contributions to the development of the United States and China as leading research-producing nations. Key study areas encompassed adaptive radiation informed by anatomical alterations, MR-Linac for enhanced vision of soft tissues, and multi-organ segmentation for accurate planning of radiotherapy. An evident increase in the frequency of phrases such as \u2018radiomics,\u2019 \u2018radiotherapy segmentation,\u2019 and \u2018dosiomics\u2019 was noted. The evaluation of AI-based software revealed a wide range of uses in several subdisciplinary fields of radiation and radiology, particularly in improving the identification of anatomical features for treatment planning and identifying organs at risk. The incorporation of AI in anatomical diagnosis in radiological imaging and radiotherapy is progressing rapidly, with substantial capacity to transform the precision of diagnoses and the effectiveness of treatment planning.<\/jats:p>","DOI":"10.1088\/2516-1091\/adc85e","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T22:54:57Z","timestamp":1743634497000},"page":"032002","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Current trends and emerging themes in utilizing artificial intelligence to enhance anatomical diagnostic accuracy and efficiency in radiotherapy"],"prefix":"10.1088","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5751-9263","authenticated-orcid":true,"given":"Salvatore","family":"Pezzino","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tonia","family":"Luca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariacarla","family":"Castorina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Puleo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0745-4107","authenticated-orcid":true,"given":"Sergio","family":"Castorina","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,5,19]]},"reference":[{"key":"prgbadc85ebib1","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1016\/j.clon.2024.06.005","article-title":"The evolving role of artificial intelligence in radiotherapy treatment planning\u2014a literature review","volume":"36","author":"Kalsi","year":"2024","journal-title":"Clin. 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