ReviewFrontiers of medicine2020
Artificial intelligence in radiotherapy: a technological review.
Review in Frontiers of medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 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
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- Synergistic effects of radiotherapy and immunotherapy: improving oncological outcomes.Frontiers in immunology · 2026Review
- Key technologies and challenges in online adaptive radiotherapy for lung cancer.Chinese medical journal · 2025Review
- Cancer Pain: Radiotherapy as a Double-Edged Sword.International journal of molecular sciences · 2025Review
- Replacing manual planning with automatic iterative planning for locally advanced rectal cancer VMAT treatment.Journal of applied clinical medical physics · 2025Article
- A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023.Radiation oncology (London, England) · 2024Article
- Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review.Ewha medical journal · 2024Review
- Charged Gold Nanoparticles for Target Identification-Alignment and Automatic Segmentation of CT Image-Guided Adaptive Radiotherapy in Small Hepatocellular Carcinoma.Nano letters · 2024Article
- Progression in low-intensity ultrasound-induced tumor radiosensitization.Cancer medicine · 2024Review
- The Radiation Therapist profession through the lens of new technology: A practice development paper based on the ESTRO Radiation Therapist Workshops.Technical innovations & patient support in radiation oncology · 2024Article
- Dimensionality Reduction and Nearest Neighbors for Improving Out-of-Distribution Detection in Medical Image Segmentation.The journal of machine learning for biomedical imaging · 2024Article
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.Journal of applied clinical medical physics · 2023Article
- Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.Current medical imaging · 2023Review
- The influence of artificial intelligence on the work of the medical physicist in radiotherapy practice: a short review.BJR open · 2023Review
- Clinical validation of deep learning algorithms for radiotherapy targeting of non-small-cell lung cancer: an observational study.The Lancet. Digital health · 2022Observational
- Attention-aware 3D U-Net convolutional neural network for knowledge-based planning 3D dose distribution prediction of head-and-neck cancer.Journal of applied clinical medical physics · 2022Article
- Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT.Journal of applied clinical medical physics · 2022Review
- Deep learning-based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy.Journal of applied clinical medical physics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiation therapy (RT) is widely used to treat cancer. Technological advances in RT have occurred in the past 30 years. These advances, such as three-dimensional image guidance, intensity modulation, and robotics, created challenges and opportunities for the next breakthrough, in which artificial intelligence (AI) will possibly play important roles. AI will replace certain repetitive and labor-intensive tasks and improve the accuracy and consistency of others, particularly those with increased complexity because of technological advances. The improvement in efficiency and consistency is important to manage the increasing cancer patient burden to the society. Furthermore, AI may provide new functionalities that facilitate satisfactory RT. The functionalities include superior images for real-time intervention and adaptive and personalized RT. AI may effectively synthesize and analyze big data for such purposes. This review describes the RT workflow and identifies areas, including imaging, treatment planning, quality assurance, and outcome prediction, that benefit from AI. This review primarily focuses on deep-learning techniques, although conventional machine-learning techniques are also mentioned.
Indexed as
Identifiers
32728877What 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.