ReviewJournal of radiation research2024
Applications of artificial intelligence for machine- and patient-specific quality assurance in radiation therapy: current status and future directions.
Review in Journal of radiation research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI screening of nuclear medicine safety breaches: patterns, causes, and opportunities for improved protocols: a systematic review.Annals of nuclear medicine · 2026Pooled it
- Recent advances in applying machine learning to proton radiotherapy.Biomedical physics & engineering express · 2025Pooled it
- Optimizing the delivery of radiotherapy with artificial intelligence.Nature reviews. Clinical oncology · 2026Review
- AI-driven troubleshooting for TrueBeam systems: Development and testing of a GPT-4.1 Chatbot.Technical innovations & patient support in radiation oncology · 2026Article
- Artificial Intelligence in Particle Therapy Treatment Planning: Current Advances and Future Directions.International journal of particle therapy · 2026Article
- Explainable machine learning for patient-specific quality assurance in intensity-modulated radiotherapy based on anatomical structures.Journal of applied clinical medical physics · 2026Article
- 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
- Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support.Translational cancer research · 2026Review
- Prediction of VMAT gamma passing rates using 3D CNNs based on leaf position analysis and gradient class activation mapping for plan complexity evaluation.Medical physics · 2026Article
- A multimodal multi-scale transformer for virtual pretreatment patient-specific QA of SBRT using portal-dosimetry fluence maps.Scientific reports · 2026Article
- Commissioning and verification of a 3D Monte Carlo independent calculation software for O-ring linac systems.Journal of applied clinical medical physics · 2026Article
- Machine Learning-Based Prediction of Elekta MLC Motion with Dosimetric Validation for Virtual Patient-Specific QA.Bioengineering (Basel, Switzerland) · 2025Article
- A pathway away from measurement-based patient-specific QA: A review of contemporary quality assurance for external-beam radiotherapy sub-systems.Journal of applied clinical medical physics · 2025Review
- Development of a database and analytical environment for precision quality management in high-precision radiotherapy.Journal of applied clinical medical physics · 2025Article
- Quality assurance in machine Learning: Do we hit the mark for total joint arthroplasty models? A systematic review.Journal of clinical orthopaedics and trauma · 2025Review
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions.Cureus · 2025Review
- A Time-Series Approach for Machine Learning-Based Patient-Specific Quality Assurance of Radiosurgery Plans.Bioengineering (Basel, Switzerland) · 2025Article
- Real-Time Dose-Guided Radiation Therapy.International journal of radiation oncology, biology, physics · 2025Review
- Detection of the failed-tolerance causes of electronic-portal-imaging-device-basedPhysics and imaging in radiation oncology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Machine- and patient-specific quality assurance (QA) is essential to ensure the safety and accuracy of radiotherapy. QA methods have become complex, especially in high-precision radiotherapy such as intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), and various recommendations have been reported by AAPM Task Groups. With the widespread use of IMRT and VMAT, there is an emerging demand for increased operational efficiency. Artificial intelligence (AI) technology is quickly growing in various fields owing to advancements in computers and technology. In the radiotherapy treatment process, AI has led to the development of various techniques for automated segmentation and planning, thereby significantly enhancing treatment efficiency. Many new applications using AI have been reported for machine- and patient-specific QA, such as predicting machine beam data or gamma passing rates for IMRT or VMAT plans. Additionally, these applied technologies are being developed for multicenter studies. In the current review article, AI application techniques in machine- and patient-specific QA have been organized and future directions are discussed. This review presents the learning process and the latest knowledge on machine- and patient-specific QA. Moreover, it contributes to the understanding of the current status and discusses the future directions of machine- and patient-specific QA.
Indexed as
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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.