ArticleStrahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]2025
Contouring in transition: perceptions of AI-based autocontouring by radiation oncologists and medical physicists in German-speaking countries.
Article in Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al], 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Mapping patients' and professionals' perceptions of artificial intelligence in radiotherapy: a scoping review.Technical innovations & patient support in radiation oncology · 2026Review
- Quantifying manual editing time in autocontouring via audit log analysis.Physics and imaging in radiation oncology · 2026Article
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI)-based autocontouring software has the potential to revolutionize radiotherapy planning. In recent years, several AI-based autocontouring solutions with many advantages have emerged; however, their clinical use raises several challenges related to implementation, quality assurance, validation, and training. The aim of this study was to investigate the current use of AI-based autocontouring software and the associated expectations and hopes of radiation oncologists and medical physicists in German-speaking countries.
methodsA digital survey consisting of 24 questions including single-choice, multiple-choice, free-response, and five-point Likert scale rankings was conducted using the online tool umfrageonline.com (enuvo GmbH, Pfäffikon SZ, Switzerland).
resultsA total of 163 participants completed the survey, with approximately two thirds reporting use of AI-based autocontouring software in routine clinical practice. Of the users, 92% found the software helpful in clinical practice. More than 90% reported using AI solutions to contour organs at risk (OARs) in the brain, head and neck, thorax, abdomen, and pelvis. The majority (88.8%) reported time savings in OAR delineation, with approximately 41% estimating savings of 11-20 min per case. However, nearly half of the respondents expressed concern about the potential degradation of resident training in sectional anatomy understanding. Of respondents, 60% would welcome guidelines for implementation and use of AI-based contouring aids from their respective radiation oncology societies. Respondents' free-text comments emphasized the need for careful monitoring and postprocessing of AI-delivered autocontours as well as concerns about overreliance on AI and its impact on the development of young physicians' contouring and planning skills.
conclusionArtificial intelligence-based autocontouring software shows promise for integration into radiation oncology workflows, with respondents recognizing its potential for time saving and standardization. However, successful implementation will require ongoing education and curriculum adaptation to ensure AI enhances, rather than replaces, clinical expertise.
Indexed as
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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.