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References from Mapping patients’ and professionals’ perceptions of artificial intelligence in radiotherapy: a scoping review. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1186/s13014-024-02551-1
10.1186/s13014-024-02551-1 · External reference
Automated segmentation in planning-CT for breast cancer radiotherapy: a review of recent advances
10.1016/j.radonc.2024.110615 · 2025 · External reference
Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer
10.1016/j.radonc.2019.03.004 · 2019 · External reference
An open source auto-segmentation algorithm for delineating heart and substructures – development and validation within a multicenter lung cancer cohort
10.1016/j.radonc.2023.110065 · 2024 · External reference
Artificial intelligence-supported applications in head and neck cancer radiotherapy treatment planning and dose optimisation
10.1016/j.radi.2023.02.018 · 2023 · External reference
Advancing prostate cancer treatment: a review of CT and MR-guided online adaptive radiotherapy techniques
10.1016/j.semradonc.2025.04.007 · 2025 · External reference
Identification of risk of cardiovascular disease by automatic quantification of coronary artery calcifications on radiotherapy planning CT scans in patients with breast cancer
10.1001/jamaoncol.2021.1144 · 2021 · External reference
Automated CBCT-based tumor volume tracking is prognostic in virally-mediated head and neck cancer
10.1016/j.ijrobp.2025.07.1280 · 2025 · External reference
Artificial intelligence in radiotherapy: a technological review
10.1007/s11684-020-0761-1 · 2020 · External reference
Global radiotherapy demands and corresponding radiotherapy-professional workforce requirements in 2022 and predicted to 2050: a population-based study
10.1016/s2214-109x(24)00355-3 · 2024 · External reference
Artificial intelligence in healthcare: transforming the practice of medicine
10.7861/fhj.2021-0095 · 2021 · External reference
Artificial intelligence in radiotherapy: current applications and future trends
10.1016/j.diii.2024.06.001 · 2024 · External reference
Patient attitudes toward artificial intelligence in cancer care: scoping review
10.2196/74010 · 2025 · External reference
Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review
10.1016/s2589-7500(21)00132-1 · 2021 · External reference
Facilitators and barriers to implementing AI in routine medical imaging: systematic review and qualitative analysis
10.2196/63649 · 2025 · External reference
PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation
10.7326/m18-0850 · 2018 · External reference
Unresolved reference
2024 · External reference
10.1097/01.naj.0000446779.99522.f6
10.1097/01.naj.0000446779.99522.f6 · External reference
Attitudes toward AI-generated risk prediction in patients with early breast cancer: an international multicenter survey
10.2340/1651-226x.2025.44030 · 2025 · External reference
Patient views on the implementation of artificial intelligence in radiotherapy
10.1016/j.radi.2023.03.006 · 2023 · External reference
Radiation oncology patients’ perceptions of artificial intelligence and machine learning in cancer care: a multi-centre cross-sectional study
10.1016/j.radonc.2025.110891 · 2025 · External reference
Patient- and clinician-based evaluation of large language models for patient education in prostate cancer radiotherapy
10.1007/s00066-024-02342-3 · 2025 · External reference
Cautiously optimistic: a survey of radiation oncology professionals’ perceptions of automation in radiotherapy planning
10.1016/j.tipsro.2020.10.003 · 2020 · External reference
Radiation therapists’ perspectives on artificial intelligence: insights from a single institution on improving effectiveness and educational supports
2025 · External reference
Radiation therapist perceptions on how artificial intelligence may affect their role and practice
10.1002/jmrs.638 · 2023 · External reference
Artificial intelligence: the opinions of radiographers and radiation therapists in Ireland
10.1016/j.radi.2021.07.022 · 2021 · External reference
IPEM topical report: results of a 2024 UK survey of artificial intelligence in medical physics and clinical engineering
10.1088/1361-6560/ade92b · 2025 · External reference
Knowledge and perceptions of Moroccan medical physicists regarding the contribution of artificial intelligence in medical imaging and radiotherapy
10.1016/j.radmp.2025.02.003 · 2025 · External reference
Lessons on AI implementation from senior clinical practitioners: an exploratory qualitative study in medical imaging and radiotherapy in the UK
10.1016/j.jmir.2024.101797 · 2025 · External reference
A survey of medical dosimetrists’ perceptions of efficiency and consistency of auto-contouring software
10.1016/j.meddos.2022.05.003 · 2022 · External reference
Excitement and concerns of young radiation oncologists over automatic segmentation: a French perspective
10.3390/cancers15072040 · 2023 · External reference
Artificial intelligence for radiotherapy auto-contouring: current use, perceptions of and barriers to implementation
10.1016/j.clon.2023.01.014 · 2023 · External reference
A national survey on the awareness of artificial intelligence technologies among radiation oncologists-Turkish society of radiation oncology artificial intelligence and information technology group study
2023 · External reference
Professional implications of introducing artificial intelligence in healthcare: an evaluation using radiation medicine as a testing ground
10.1016/j.jmir.2018.02.006 · 2018 · External reference
Artificial intelligence in radiation therapy treatment planning: a discrete choice experiment
10.1002/jmrs.843 · 2025 · External reference
Contouring in transition: perceptions of AI-based autocontouring by radiation oncologists and medical physicists in German-speaking countries
10.1007/s00066-025-02403-1 · 2025 · External reference
Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology – national survey
10.1016/j.jmir.2020.11.013 · 2021 · External reference
Patient perspectives on the use of artificial intelligence in health care: a scoping review
10.17294/2330-0698.2029 · 2024 · External reference
Radiologists’ perceptions on AI integration: an in-depth survey study
10.1016/j.ejrad.2024.111590 · 2024 · External reference
Multi-stakeholder preferences for the use of artificial intelligence in healthcare: a systematic review and thematic analysis
10.1016/j.socscimed.2023.116357 · 2023 · External reference