ArticlePhysics and imaging in radiation oncology2020
Machine learning applications in radiation oncology: Current use and needs to support clinical implementation.
Article in Physics and imaging in radiation oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Implementing AI in Radiotherapy: Insights From the Healthcare Professions.Sociology of health & illness · 2026Article
- Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.Systematic reviews · 2026Article
- Standardized reporting as a foundation for trustworthy and reproducible artificial intelligence in radiotherapy.Physics and imaging in radiation oncology · 2026Article
- AID-RT: Standardising Artificial Intelligence Documentation in RadioTherapy with a domain-specific model card.Physics and imaging in radiation oncology · 2026Review
- An open-source tool for converting 3D mesh volumes into synthetic DICOM CT images for medical physics research.Physical and engineering sciences in medicine · 2025Article
- Contouring in transition: perceptions of AI-based autocontouring by radiation oncologists and medical physicists in German-speaking countries.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Evaluation and tuning of a commercial automated planning system for prostate radiotherapy.Physics and imaging in radiation oncology · 2025Article
- Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment.Journal of medical radiation sciences · 2025Article
- Can Deep Learning-Based Auto-Contouring Software Achieve Accurate Pelvic Volume Delineation in Volumetric Image-Guided Radiotherapy for Prostate Cancer? A Preliminary Multicentric Analysis.Current oncology (Toronto, Ont.) · 2025Article
- Quality Management for Radiation Oncology In-House Software Products.Bioengineering (Basel, Switzerland) · 2025Article
- ESTRO guidelines for developing questionnaires in survey-based radiation oncology research.Clinical and translational radiation oncology · 2025Review
- Implementation Strategy for Artificial Intelligence in Radiotherapy: Can Implementation Science Help?JCO clinical cancer informatics · 2024Article
- A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023.Radiation oncology (London, England) · 2024Article
- Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.Radiation oncology (London, England) · 2024Article
- Evaluation of Deep Learning Clinical Target Volumes Auto-Contouring for Magnetic Resonance Imaging-Guided Online Adaptive Treatment of Rectal Cancer.Advances in radiation oncology · 2024Article
- Automation and artificial intelligence in radiation therapy treatment planning.Journal of medical radiation sciences · 2024Article
- Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients.Journal of applied clinical medical physics · 2024Article
- NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.International journal of radiation oncology, biology, physics · 2024Article
- Artificial intelligence applications in prostate cancer.Prostate cancer and prostatic diseases · 2024Review
- Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies.BJR artificial intelligence · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeThe use of artificial intelligence (AI)/ machine learning (ML) applications in radiation oncology is emerging, however no clear guidelines on commissioning of ML-based applications exist. The purpose of this study was therefore to investigate the current use and needs to support implementation of ML-based applications in routine clinical practice. MATERIALS AND
methodsA survey was conducted among medical physicists in radiation oncology, consisting of four parts: clinical applications (1), model training, acceptance and commissioning (2), quality assurance (QA) in clinical practice and General Data Protection Regulation (GDPR) (3), and need for education and guidelines (4). Survey answers of medical physicists of the same radiation oncology centre were treated as a separate unique responder in case reporting on different AI applications.
resultsIn total, 213 medical physicists from 202 radiation oncology centres were included in the analysis. Sixty-nine percent (1 4 7) was using (37%) or preparing (32%) to use ML in clinic, mostly for contouring and treatment planning. In 86%, human observers were still involved in daily clinical use for quality check of the output of the ML algorithm. Knowledge on ethics, legislation and data sharing was limited and scattered among responders. Besides the need for (implementation) guidelines, training of medical physicists and larger databases containing multicentre data was found to be the top priority to accommodate the further introduction of ML in clinical practice.
conclusionThe results of this survey indicated the need for education and guidelines on the implementation and quality assurance of ML-based applications to benefit clinical introduction.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.