ReviewMedical oncology (Northwood, London, England)2020
Artificial Intelligence in radiotherapy: state of the art and future directions.
Review in Medical oncology (Northwood, London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 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
34 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Clinical application of MR-Linac in tumor radiotherapy: a systematic review.Radiation oncology (London, England) · 2023Pooled it
- Delta radiomics: a systematic review.La Radiologia medica · 2021Pooled it
- The Application and Development of Deep Learning in Radiotherapy: A Systematic Review.Technology in cancer research & treatmentPooled it
- Clinical evaluation and regression test of a commercial deep-learning auto-segmentation model.Journal of applied clinical medical physics · 2026Article
- Surviving Cancer, Lacking Support: The Hidden Burden of Modern Radiation Oncology in the Treatment of Oligometastatic Disease.Cancers · 2026Review
- Automatic segmentation of clinical target volume for radiation therapy in breast-conserving patients and exploration of clinical factors influential to its performance.The British journal of radiology · 2026Article
- Evaluation of CNN-based deep learning models for auto-contouring in glioblastoma radiotherapy: a review.Radiation oncology (London, England) · 2025Review
- Evaluation and tuning of a commercial automated planning system for prostate radiotherapy.Physics and imaging in radiation oncology · 2025Article
- AI Revolution in Radiology, Radiation Oncology and Nuclear Medicine: Transforming and Innovating the Radiological Sciences.Journal of medical imaging and radiation oncology · 2025Review
- How Italian radiation oncologists use ChatGPT: a survey by the young group of the Italian association of radiotherapy and clinical oncology (yAIRO).La Radiologia medica · 2025Article
- Article
- A Comprehensive Survey of Deep Learning Approaches in Image Processing.Sensors (Basel, Switzerland) · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Impact of motion management strategies on abdominal organ at risk delineation for magnetic resonance-guided radiotherapy.Physics and imaging in radiation oncology · 2024Article
- Survey of research attitudes of RTTs working in Scotland: A Scottish radiographer research forum collaboration.Technical innovations & patient support in radiation oncology · 2024Article
- Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation.La Radiologia medica · 2024Article
- Advancements and Challenges in the Application of Metal-Organic Framework (MOF) Nanocomposites for Tumor Diagnosis and Treatment.International journal of nanomedicine · 2024Review
- Estimating how contouring differences affect normal tissue complication probability modelling.Physics and imaging in radiation oncology · 2024Article
- Deep learning-based classification of organs at risk and delineation guideline in pelvic cancer radiation therapy.Journal of applied clinical medical physics · 2023Article
- Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.Journal of personalized medicine · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application and Radiation oncology could benefit from these approaches, particularly in patients' medical records, imaging, baseline pathology, planning or instrumental data. Artificial Intelligence systems could simplify many steps of the complex workflow of radiotherapy such as segmentation, planning or delivery. However, Artificial Intelligence could be considered as a "black box" in which human operator may only understand input and output predictions and its application to the clinical practice remains a challenge. The low transparency of the overall system is questionable from manifold points of view (ethical included). Given the complexity of this issue, we collected the basic definitions to help the clinician to understand current literature, and overviewed experiences regarding implementation of AI within radiotherapy clinical workflow, aiming to describe this field from the clinician perspective.
Indexed as
Identifiers
32323066What 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.