ReviewPhysics and imaging in radiation oncology2026
AID-RT: Standardising Artificial Intelligence Documentation in RadioTherapy with a domain-specific model card.
Review in Physics and imaging in radiation oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
4 citing papers in PubMed.
- Development of an artificial intelligence driven dose prediction pipeline for online adaptive magnetic resonance-guided radiotherapy.Physics and imaging in radiation oncology · 2026Article
- Multimodal deep learning to support delineation of neuro-oncology organs based on the EPTN international neurological contouring atlas.Physics and imaging in radiation oncology · 2026Article
- Large language models for scalable standardization in radiation oncology.Physics and imaging in radiation oncology · 2026Article
- Standardized reporting as a foundation for trustworthy and reproducible artificial intelligence in radiotherapy.Physics and imaging in radiation oncology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Purpose: Insufficient documentation of artificial intelligence (AI) models remains a widespread issue, which hampers reproducibility in research environments and safe integration in clinical departments. Our goal was to develop a standardised, structured, and domain-specific reporting framework tailored to AI models in radiotherapy (RT), enhancing transparency and accountability. Methods: A working group was formed after the ESTRO Physics Workshop 2023, Results: The final template included 6 sections: 0) Card metadata, 1) Model basic information; 2) Model technical specifications (i.e. architecture, software and hardware); 3) Training data, methodology, and information; 4) Evaluation data, methodology, and results (a.k.a commissioning for clinical models); and 5) Other considerations, including ethical use, risk analysis, and monitoring. It is publicly available as a downloadable document template and as an interactive web-based form to facilitate information entry. Conclusions: We proposed a practical, consensus-driven template tailored to the unique requirements of AI models in RT, with applicability in both research and clinical environments, conveying the key information required for informed use.
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Identifiers
What OpenQuestion holds
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.