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Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/31280
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dc.contributor.authorRasmussen, Mathis Ersteden_US
dc.contributor.authorAkbarov, Kamalen_US
dc.contributor.authorTitovich, Egoren_US
dc.contributor.authorNijkamp, Jasper Albertusen_US
dc.contributor.authorVan Elmpt, Wouteren_US
dc.contributor.authorPrimdahl, Hanneen_US
dc.contributor.authorLassen, Pernilleen_US
dc.contributor.authorCacicedo, Jonen_US
dc.contributor.authorCordero-Mendez, Lisbethen_US
dc.contributor.authorUddin, A F M Kamalen_US
dc.contributor.authorMohamed, Ahmeden_US
dc.contributor.authorPrajogi, Benen_US
dc.contributor.authorBrohet, Kartika Eridaen_US
dc.contributor.authorNyongesa, Catherineen_US
dc.contributor.authorLomidze, Darejanen_US
dc.contributor.authorPrasiko, Gisupnikhaen_US
dc.contributor.authorFerraris, Gustavoen_US
dc.contributor.authorMahmood, Humeraen_US
dc.contributor.authorStojkovski, Igoren_US
dc.contributor.authorIsayev, Isaen_US
dc.contributor.authorMohamad, Issaen_US
dc.contributor.authorShirley, Leivonen_US
dc.contributor.authorKochbati, Lotfien_US
dc.contributor.authorEftodiev, Ludmilaen_US
dc.contributor.authorPiatkevich, Maksimen_US
dc.contributor.authorBonilla Jara, Maria Matildeen_US
dc.contributor.authorSpahiu, Orgesen_US
dc.contributor.authorAralbayev, Rakhaten_US
dc.contributor.authorZakirova, Raushanen_US
dc.contributor.authorSubramaniam, Sandyaen_US
dc.contributor.authorKibudde, Solomonen_US
dc.contributor.authorTsegmed, Uranchimegen_US
dc.contributor.authorKorreman, Stine Sofiaen_US
dc.contributor.authorEriksen, Jesper Grauen_US
dc.date.accessioned2024-09-10T06:39:05Z-
dc.date.available2024-09-10T06:39:05Z-
dc.date.issued2024-09-05-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/31280-
dc.description.abstractMost research on artificial intelligence-based auto-contouring as template (AI-assisted contouring) for organs-at-risk (OARs) stem from high-income countries. The effect and safety are, however, likely to depend on local factors. This study aimed to investigate the effects of AI-assisted contouring and teaching on contouring time and contour quality among radiation oncologists (ROs) working in low- and middle-income countries (LMICs).en_US
dc.language.isoenen_US
dc.publisherLippincott, Williams & Wilkinsen_US
dc.relation.ispartofJCO Global Oncologyen_US
dc.subjecte-learningen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectAssisted Contouring Skills in Radiotherapyen_US
dc.titlePotential of E-Learning Interventions and Artificial Intelligence-Assisted Contouring Skills in Radiotherapy: The ELAISA Studyen_US
dc.typeArticleen_US
dc.identifier.doi10.1200/GO.24.00173-
dc.identifier.volume10-
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Medicine-
Appears in Collections:Faculty of Medicine: Journal Articles
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