ReviewJournal of cancer education : the official journal of the American Association for Cancer Education2025
Enhancing Radiation Oncology Education Through Artificial Intelligence: A Review of Applications, Limitations, and Future Directions.
Review in Journal of cancer education : the official journal of the American Association for Cancer Education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers 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
- Transforming surgical ward rounds: constructivist pedagogy, structured models, and intelligent technologies.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence [AI] is increasingly integrated into radiation oncology practice, from auto-contouring and treatment planning to decision support. However, formal residency training has not kept pace with these advances, leaving educational gaps in preparing future radiation oncologists for AI-informed clinical practice. This review aims to review current applications of AI in radiation oncology and evaluate how AI-driven tools can enhance resident education in clinical, procedural, and research domains. A narrative literature review was conducted across major databases [MEDLINE, EMBASE, CENTRAL, CINAHL] using keywords including "artificial intelligence," "medical education," "radiation oncology," and "auto-contouring." Expert commentary and selected studies on educational implementation of AI were included. AI enhanced learning tools span auto-segmentation feedback systems, plan optimization simulators and clinical decision support engines. AI improves access to complex cases, supports real-time feedback, and reduces dependence on faculty availability. However, risks include overreliance, algorithmic bias, and misinterpretation of AI generated outputs. Residents must develop the skills to critically appraise AI tools, review outputs, and integrate patient-centered decision making. AI offers significant potential to transform resident education in radiation oncology. Structured curriculum integration can enhance training while preserving core clinical judgment. Faculty development and institutional support are critical to successful implementation.
Indexed as
Identifiers
41108512What 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.