ArticleJournal of applied clinical medical physics2023
Machine learning-based radiotherapy time prediction and treatment scheduling management.
Article in Journal of applied clinical medical physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- AI-driven troubleshooting for TrueBeam systems: Development and testing of a GPT-4.1 Chatbot.Technical innovations & patient support in radiation oncology · 2026Article
- Research progress of machine learning applications in gastric cancer diagnosis and therapy.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 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
- Longitudinal evaluation of workflow optimization in radiotherapy: A 4-year retrospective study.Journal of applied clinical medical physics · 2025Article
- Developing a hybrid machine learning model to predict treatment time duration as a workflow regulation tool in public and private dental clinics.Scientific reports · 2025Article
- Artificial intelligence in radiation therapy: from imaging to delivery-a comprehensive review.BJR artificial intelligence · 2025Review
- Machine learning-based radiotherapy time prediction and treatment scheduling management.Journal of applied clinical medical physics · 2023Article
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
Abstract
purposeThe utility efficiency of medical devices is important, especially for countries such as China, which have a large population and shortage of medical care resources. Radiotherapy devices are among the most valuable and specialized equipment categories and carry enormous treatment loads. In this study, a novel method is proposed to improve the efficiency of a radiotherapy device (linac). Although scheduling management with accurate prediction of the entire treatment time included in each appointment, arrange a reasonable time duration for appointments and save time between patient shifts effectively. Tasks belonging to the treatment and non-treatment groups can be assigned more flexibly based on the availability of time. MATERIAL AND
methodsData from 1665 patients, including patient positioning time (PT) and treatment time (TT), were collected in collaboration with the Radiotherapy Center of the Department of Oncology at the Second Affiliated Hospital of Kunming Medical University from November 2020 to August 2021. The features related to PT and TT were extracted and used to train the machine learning-based model to predict PT and TT in independent patients. The prediction results were subsequently applied to a minute-based scheduling tool.
conclusionArtificial intelligence is a promising approach to solve abstract problems with a specialized knowledge background. The results of this study show encouraging prediction outcomes in relation to effective scheduling management and could improve the efficiency of the linac. This successful trial broadens the meaning of medical data and potential future research directions in radiotherapy.
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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.