ReviewJournal of personalized medicine2023
Enhancing Radiotherapy Workflow for Head and Neck Cancer with Artificial Intelligence: A Systematic Review.
Review in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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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
10 citing papers in PubMed, 14 citations in OpenAlex.
- Clinical challenges in the adaptation of AI predictive models in radiation oncology for gynaecological cancer: a systematic review by the radiation oncology-AI MITO group.La Radiologia medica · 2026Review
- Review
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- The Evolving Role of Radiation Therapy Technologists in Head and Neck Cancer: A Narrative Review and Operational Framework.Journal of imaging · 2026Review
- Voxel-Based Dose-Toxicity Modeling for Predicting Post-Radiotherapy Toxicity: A Critical Perspective.Journal of clinical medicine · 2025Article
- Longitudinal evaluation of workflow optimization in radiotherapy: A 4-year retrospective study.Journal of applied clinical medical physics · 2025Article
- Three-dimensional in vitro models in head and neck cancer: current trends and applications.Medical oncology (Northwood, London, England) · 2025Review
- The Future of Oral Oncology: How Artificial Intelligence is Redefining Surgical Procedures and Patient Management.International dental journal · 2025Review
- Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNet.Head and Neck Tumor Segmentation for MR-Guided Applications : First MICCAI Challenge, HNTS-MRG 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, proceedings · 2025Article
- The Integration of Deep Learning in Radiotherapy: Exploring Challenges, Opportunities, and Future Directions through an Umbrella Review.Diagnostics (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHead and neck cancer (HNC) is characterized by complex-shaped tumors and numerous organs at risk (OARs), inducing challenging radiotherapy (RT) planning, optimization, and delivery. In this review, we provided a thorough description of the applications of artificial intelligence (AI) tools in the HNC RT process.
methodsThe PubMed database was queried, and a total of 168 articles (2016-2022) were screened by a group of experts in radiation oncology. The group selected 62 articles, which were subdivided into three categories, representing the whole RT workflow: (i) target and OAR contouring, (ii) planning, and (iii) delivery.
resultsThe majority of the selected studies focused on the OARs segmentation process. Overall, the performance of AI models was evaluated using standard metrics, while limited research was found on how the introduction of AI could impact clinical outcomes. Additionally, papers usually lacked information about the confidence level associated with the predictions made by the AI models.
conclusionsAI represents a promising tool to automate the RT workflow for the complex field of HNC treatment. To ensure that the development of AI technologies in RT is effectively aligned with clinical needs, we suggest conducting future studies within interdisciplinary groups, including clinicians and computer scientists.
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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.