SynthesisAdvances in therapy2023
Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews.
Synthesis in Advances in therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 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
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning based prediction of recurrence in oral tongue cancer: a systematic review with quantitative synthesis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Pooled it
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Effectiveness of radiotherapy or chemoradiotherapy in older patients with advanced-stage, non-surgically treated head and neck cancer: A machine learning analysis.Medical oncology (Northwood, London, England) · 2026Article
- Preoperative Frailty-Inflammation Phenotypes and Early Functional and Patient-Reported Recovery After Head-and-Neck Oncologic Reconstruction: An Exploratory Prospective Single-Center Cohort Study.Journal of clinical medicine · 2026Article
- Article
- Deep learning-based computed tomography detection of early lymph node metastasis in head and neck cancer.Quantitative imaging in medicine and surgery · 2026Article
- Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.Cancers · 2026Review
- Deep learning classification of nasal anatomical variants on computed tomography for endoscopic sinus surgery.Scientific reports · 2026Article
- Review
- Review
- Hybrid Reconstruction in Head and Neck Surgery: Integration of Virtual Planning, Navigation, and Robotic Microsurgery.Journal of clinical medicine · 2026Review
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- Integrating transcriptomic data and artificial intelligence to personalize curative treatments for head and neck cancer patients.NPJ precision oncology · 2026Review
- Selection Criteria for De-Escalated Chemoradiotherapy for HPV-Related Oropharyngeal Cancer Based on Prognostic Biomarkers or Early Tumor Response to Therapy: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- The incidence of avid lesions in head and neck cancer patients undergoing positron emission tomography-computed tomography scanning.The Journal of laryngology and otology · 2026Article
- Evaluating AI Performance in Systematic Literature Reviews for HEOR: A Case Study.Journal of health economics and outcomes research · 2026Article
- Article
- Ultrasound-Based Deep Learning Radiomics to Predict Cervical Lymph Node Metastasis in Major Salivary Gland Carcinomas.International dental journal · 2025Article
- Machine learning for survival outcome in head and neck squamous cell carcinoma: a multicenter validation study.Scientific reports · 2025Observational
- Evaluation of Deep Learning Convolutional Neural Networks for Classification of Carcinoma Ex Pleomorphic Adenoma and Pleomorphic Adenoma in Whole-Slide Images.Head and neck pathology · 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
14 authors.
Funding
Abstract
introductionSeveral studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management.
methodsElectronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines.
resultsOf the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks.
conclusionAt present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.
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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.