ReviewBJR artificial intelligence2025
Artificial intelligence in radiation therapy: from imaging to delivery-a comprehensive review.
Review in BJR artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Clinical challenges in the adaptation of AI predictive models in radiation oncology for gynaecological cancer: a systematic review by the radiation oncology-AI MITO group.La Radiologia medica · 2026Review
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- Artificial Intelligence Integration in Radiation Therapy Education: A Multi-Modal Approach.Journal of medical radiation sciences · 2026Article
- Synergistic effects of radiotherapy and immunotherapy: improving oncological outcomes.Frontiers in immunology · 2026Review
- Can Advances in Artificial Intelligence Strengthen the Role of Intraoperative Radiotherapy in the Treatment of Cancer?Cancers · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is revolutionizing radiotherapy by enhancing precision, efficiency, and personalization in patient care. AI offers innovative solutions to longstanding challenges in radiation oncology by streamlining workflows, improving treatment accuracy, and alleviating the burden of heavy workloads on healthcare professionals, which is particularly critical in addressing current staffing shortages. As AI continues to evolve, its applications in radiotherapy are expected to expand, underscoring the need for healthcare providers to understand its current uses and future potential. This review provides a comprehensive analysis of AI's role across all stages of the radiotherapy workflow, from imaging and treatment planning to quality assurance and delivery. By focusing on recent advancements and key areas of implementation, the review highlights how AI is transforming clinical workflows and improving patient outcomes. Additionally, this review addresses the critical topic of AI regulations and guidelines, which are essential for its safe and ethical integration into clinical practice. It also explores AI's applications in clinical trials, emphasizing its role in enhancing trial efficiency and reliability. By examining these aspects, this review offers a detailed overview of the current state of AI in radiotherapy and its potential to shape the future of cancer treatment.
Indexed as
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.