ReviewTechnology in cancer research & treatment2019
Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.
Review in Technology in cancer research & treatment, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers, 1 of them a synthesis 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
115 citing papers in PubMed, 1 synthesis or guideline pooled it, 230 citations in OpenAlex.
- Operating Room Performance Optimization Metrics: a Systematic Review.Journal of medical systems · 2023Pooled it
- Machine learning-based radiomics nomograms to predict number of fields in postoperative IMRT for breast cancer.Journal of applied clinical medical physics · 2024Trial
- Clinical evaluation and regression test of a commercial deep-learning auto-segmentation model.Journal of applied clinical medical physics · 2026Article
- Automated planning architectures for online adaptive radiotherapy: A narrative review of the ethos and unity/Monaco platforms.Journal of applied clinical medical physics · 2026Review
- PlanningCopilot: An agentic framework integrating ESAPI modules for autonomous treatment planning in lung radiotherapy.Medical physics · 2026Article
- Artificial Intelligence in Particle Therapy Treatment Planning: Current Advances and Future Directions.International journal of particle therapy · 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
- Assessing the Accuracy and Precision of Artificial Intelligence for Diabetes Mellitus and Hypertension Management.Journal of clinical medicine · 2026Article
- Artificial intelligence in radiation treatment planning: a narrative review from automation to clinical decision support.Translational cancer research · 2026Review
- Review
- Deep Reinforcement Learning-Based Automated Treatment Planning for a Rotating Gamma System.Cureus · 2026Article
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Development and clinical deployment of an automated planning tool for prostate only and male whole pelvis plans based on multi-criteria optimization.Journal of applied clinical medical physics · 2026Article
- Federated learning-driven intelligent framework for multi-center radiotherapy dose distribution prediction oriented toward linear accelerators.Scientific reports · 2026Article
- A tandem reinforcement learning framework for localized prostate cancer treatment planning and machine parameter optimization.Medical physics · 2026Article
- Beyond auto-segmentation: the case for planning and dosimetry AI in head and neck radiation oncology.BMJ oncology · 2026Review
- Trustworthy artificial intelligence in radiation oncology: cross-industry lessons for development, validation, and deployment.Frontiers in oncology · 2026Review
- Ultrasound-based artificial intelligence for breast lesion classification.Frontiers in oncology · 2026Review
- Personalized intensity modulation radiation therapy auto-planning for esophageal cancer.Scientific reports · 2025Article
- New insights into automatic treatment planning for cancer radiotherapy using explainable artificial intelligence.Physics in medicine and biology · 2025Article
55 more citing papers are in PubMed but not listed here.
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 at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works.
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.