{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T19:45:49Z","timestamp":1792611949332,"version":"4.5.0"},"reference-count":11,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>While international surveys have examined radiation oncology professionals\u2019 attitudes toward artificial intelligence (AI) and documented generally low early utilization, actual implementation patterns and the depth of integration among engaged practitioners remain unclear. This multi\u2010institutional survey aims to characterize current AI implementation, identify critical barriers to optimization, and evaluate the psychological factors driving clinical trust.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and materials<\/jats:title>\n                    <jats:p>\n                      A de\u2010identified survey covering six domains was developed by a multi\u2010institutional working group and distributed via a widely subscribed medical physics listserv and at the 2025 American Association of Physicists in Medicine (AAPM) Annual Meeting. The instrument assessed adoption rates, specific workflow applications, and user perceptions (trust and understanding) using 6\u2010point Likert scales to reduce central tendency bias. Quantitative data were analyzed using Pearson correlation coefficients and Welch's two\u2010sample\n                      <jats:italic>t<\/jats:italic>\n                      \u2010tests, while qualitative responses underwent inductive thematic analysis (\u03ba\u00a0=\u00a00.78).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      In this convenience sample of 80 respondents (46.3% radiation oncologists, 37.5% medical physicists), 85.0% (68\/80) reported current AI use. Adoption is heavily concentrated in auto\u2010contouring (88.2% of AI users), whereas diagnosis\/prognostication (14.7%) and treatment planning (23.5%) remain limited. Most users (60.3%) employ AI for task automation without influencing clinical judgment. Trust in AI (3.55\u00a0\u00b1\u00a01.02) correlates positively with perceived time savings (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.57), clarity of use (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.54), and technical understanding (\n                      <jats:italic>r<\/jats:italic>\n                      \u00a0=\u00a00.51, all\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001), with no significant difference among these correlations (Williams test, both\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&gt;\u00a00.59). Notably, self\u2010rated understanding averaged near the scale midpoint (3.44\u00a0\u00b1\u00a01.22), yet 88.8% welcomed greater AI integration. Qualitative analysis identified transparency (38%) and validation protocols (31%) as the primary barriers to deeper implementation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AI use was common within this sample (85.0%), although self\u2010selection likely inflates this figure relative to the broader field. However, implementation remains \u201cbroad but shallow,\u201d focused on workflow automation rather than clinical intelligence. The gap between enthusiasm and understanding necessitates a shift toward active institutional governance and standardized AI\u2010literacy education.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70871","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T19:23:16Z","timestamp":1792610596000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ROECSG report: Real\u2010world implementation patterns and perceptions of artificial intelligence in radiation oncology"],"prefix":"10.1002","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7209-4104","authenticated-orcid":false,"given":"James J","family":"Sohn","sequence":"first","affiliation":[{"name":"Department of Radiation Medicine University of Wisconsin  Madison Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cory","family":"Knill","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Michigan  Ann Arbor Michigan USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Bossart","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Miami Miller School of Medicine  Miami Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Alabama at Birmingham  Birmingham Alabama USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rohan K","family":"Patel","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Medical College of Wisconsin  Milwaukee Wisconsin USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Therapeutic Radiology Yale University New Haven Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leah M","family":"Katz","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Miami Miller School of Medicine  Miami Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"American Society for Radiation Oncology.2023Member Survey: Workforce and Payor Issues. astro.org. Accessed January 2025."},{"key":"e_1_2_11_3_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.tipsro.2021.03.004","article-title":"Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: a New Zealand study","volume":"18","author":"Mugabe KV","year":"2021","journal-title":"Tech Innov Patient Support Radiat Oncol"},{"key":"e_1_2_11_4_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.tipsro.2020.10.003","article-title":"Cautiously optimistic: a survey of radiation oncology professionals\u2019 perceptions of automation in radiotherapy planning","volume":"16","author":"Batumalai V","year":"2020","journal-title":"Tech Innov Patient Support Radiat Oncol"},{"issue":"1","key":"e_1_2_11_5_1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.jmir.2020.11.013","article-title":"Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology\u2014national survey","volume":"52","author":"Wong K","year":"2021","journal-title":"J Med Imaging Radiat Sci"},{"key":"e_1_2_11_6_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.radonc.2024.110345","article-title":"A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy","volume":"197","author":"Hurkmans C","year":"2024","journal-title":"Radiother Oncol"},{"issue":"10","key":"e_1_2_11_7_1","doi-asserted-by":"crossref","first-page":"e537","DOI":"10.1016\/S2589-7500(20)30218-1","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT\u2010AI extension","volume":"2","author":"Liu X","year":"2020","journal-title":"Lancet Digit Health"},{"key":"e_1_2_11_8_1","article-title":"ESTRO guidelines for developing questionnaires in survey based radiation oncology research","volume":"51","author":"Rattani A","year":"2024","journal-title":"Clin Transl Radiat Oncol"},{"key":"e_1_2_11_9_1","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclinepi.2025.111675","article-title":"Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review","volume":"180","author":"Tsegaye B","year":"2025","journal-title":"J Clin Epidemiol"},{"issue":"2","key":"e_1_2_11_10_1","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.prro.2018.12.003","article-title":"Standardizing normal tissue contouring for radiation therapy treatment planning: an ASTRO consensus paper","volume":"9","author":"Wright JL","year":"2019","journal-title":"Pract Radiat Oncol"},{"key":"e_1_2_11_11_1","unstructured":"AchiamJ AdlerS AgarwalS et\u00a0al.GPT\u20104 technical report. arXiv:2303.08774.2023."},{"key":"e_1_2_11_12_1","unstructured":"BookC.Practical considerations for the use of artificial intelligence in radiation oncology. itnonline.com. Published March 6 2024."}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70871","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/acm2.70871","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70871","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T19:23:17Z","timestamp":1792610597000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.70871"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,21]]},"references-count":11,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2026,11]]}},"alternative-id":["10.1002\/acm2.70871"],"URL":"https:\/\/doi.org\/10.1002\/acm2.70871","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,21]]},"assertion":[{"value":"2026-07-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70871"}}