{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T11:07:23Z","timestamp":1787828843474,"version":"build-2784847793"},"reference-count":39,"publisher":"Wiley","issue":"S2","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Medical Radiation Sci"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>The application of artificial intelligence (AI) in radiation therapy holds promise for addressing challenges, such as healthcare staff shortages, increased efficiency and treatment planning variations. Increased AI adoption has the potential to standardise treatment protocols, enhance quality, improve patient outcomes, and reduce costs. However, drawbacks include impacts on employment and algorithmic biases, making it crucial to navigate trade\u2010offs. A discrete choice experiment (DCE) was undertaken to examine the AI\u2010related characteristics radiation oncology professionals think are most important for adoption in radiation therapy treatment planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Radiation oncology professionals completed an online discrete choice experiment to express their preferences about AI systems for radiation therapy planning which were described by five attributes, each with 2\u20134 levels: accuracy, automation, exploratory ability, compatibility with other systems and impact on workload. The survey also included questions about attitudes to AI. Choices were modelled using mixed logit regression.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The survey was completed by 82 respondents. The results showed they preferred AI systems that offer the largest time saving, and that provide explanations of the AI reasoning (both in\u2010depth and basic). They also favoured systems that provide improved contouring precision compared with manual systems. Respondents emphasised the importance of AI systems being cost\u2010effective, while also recognising AI's impact on professional roles, responsibilities, and service delivery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This study provides important information about radiation oncology professionals' priorities for AI in treatment planning. The findings from this study can be used to inform future research on economic evaluations and management perspectives of AI\u2010driven technologies in radiation therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmrs.843","type":"journal-article","created":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T12:39:35Z","timestamp":1734698375000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment"],"prefix":"10.1002","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9068-9504","authenticated-orcid":false,"given":"Milena","family":"Lewandowska","sequence":"first","affiliation":[{"name":"Centre for Health Economics Research and Evaluation University of Technology Sydney  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4476-0656","authenticated-orcid":false,"given":"Deborah","family":"Street","sequence":"additional","affiliation":[{"name":"Centre for Health Economics Research and Evaluation University of Technology Sydney  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3527-260X","authenticated-orcid":false,"given":"Jackie","family":"Yim","sequence":"additional","affiliation":[{"name":"Centre for Health Economics Research and Evaluation University of Technology Sydney  Sydney New South Wales Australia"},{"name":"Radiation Oncology Royal North Shore Hospital  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8310-6393","authenticated-orcid":false,"given":"Scott","family":"Jones","sequence":"additional","affiliation":[{"name":"Radiation Oncology Princess Alexandra Hospital Raymond Terrace  Brisbane Queens Land Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0039-9635","authenticated-orcid":false,"given":"Rosalie","family":"Viney","sequence":"additional","affiliation":[{"name":"Centre for Health Economics Research and Evaluation University of Technology Sydney  Sydney New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"e_1_2_11_2_1","unstructured":"Australian Institute of Health and Welfare.Cancer in Australia 2021. 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