ArticleJournal of medical radiation sciences2026
Artificial Intelligence Integration in Radiation Therapy Education: A Multi-Modal Approach.
Article in Journal of medical radiation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- DEGRO consensus framework for undergraduate radiation therapy teaching in Germany: a white paper with integrated guidance on artificial intelligence in medical education.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionCritical workforce shortages in radiation oncology have led tertiary institutions to rapidly expand their radiation therapy (RT) student cohorts. The increase in students entering university created the challenge of scaling educational delivery while preserving the quantity and quality of learning essential for developing well-prepared, competent clinicians. Artificial Intelligence (AI) applications have been recognised as valuable tools in medical education; however, limited research has addressed their use in RT education. This study aimed to evaluate three AI educational innovations used in the RT degree at the University of Newcastle.
methodsA cross-sectional study design was implemented to investigate the perceptions of students toward the integration of three AI educational innovations, which were embedded in the RT program curriculum across Years 1, 2 and 3. The three innovations included: (1) delivery innovation (AI video lectures), (2) assessment innovation (AI-assisted assessment feedback), and (3) content innovation (AI-simulated communication tasks). Descriptive statistics were calculated for quantitative survey responses. Open-ended responses were analysed to find recurring themes.
resultsA total of 62 students participated in the study across three cohorts (Years 1 (n = 33), 2 (n = 13) and 3 (n = 16)), with a mean age of 20.6 years. In Year 1, n = 24 (73%) of students reported being 'satisfied' or 'very satisfied' with the AI video format. Among Year 2 students, n = 7 (54%) wanted AI feedback on future assessments, while n = 6 (46%) were unsure or opposed to future AI feedback. In Year 3, n = 12 (75%) felt 'more' or 'much more comfortable' practising with the AI patient than with peers.
conclusionThis research revealed varied outcomes related to AI innovations among year groups. The integration of AI was perceived positively; however, participants favoured AI tools for formative learning over those used for assessments.
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.