ReviewInternational dental journal2022
The Effectiveness of Artificial Intelligence in Detection of Oral Cancer.
Review in International dental journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 5 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
52 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Emerging applications of artificial intelligence for risk stratification in head and neck cancer: a scoping review.Frontiers in oncology · 2026Pooled it
- Systematic Review and Meta-Analysis on the Accuracy of Artificial Intelligence Algorithms in Individuals Gender Detection Using Orthopantomograms.International dental journal · 2025Pooled it
- Development of Artificial Intelligence Models for Tooth Numbering and Detection: A Systematic Review.International dental journal · 2024Pooled it
- Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis.Clinical oral investigations · 2024Pooled it
- Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis.Oral radiology · 2024Pooled it
- Portable Deep Learning-Assisted Device for Automated Interincisal Opening Assessment and Oral Submucous Fibrosis Lesion Identification: A Translational Clinical Study.Journal of imaging informatics in medicine · 2026Article
- Enhanced Histopathologic Image Analysis for Mouth Cancer Classification Using Morphological Reconstruction and UNet.Journal of imaging informatics in medicine · 2026Article
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- Oral cancer in the era of precision medicine: molecular targets and technological innovations.Scientific reports · 2026Article
- SSDA_AOA: Stacked Sparse Denoising Autoencoder With Archimedes Optimization Algorithm Based Oral Cancer Detection on Histopathological Images.Oral diseases · 2026Article
- Evolving Landscape for Artificial Intelligence in the Diagnosis and Histopathologic Assessment of Oral and Oropharyngeal Cancer.Head and neck pathology · 2026Review
- MeMoSA dataset: A multi-country collection of over 30,000 oral mucosa images with clinically labelled lesions.Scientific data · 2026Article
- AI-Powered Microscopic Diagnostic Techniques forJournal of dentistry (Shiraz, Iran) · 2026Article
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- Artificial intelligence in gastric cancer research: a bibliometric and visualized analysis from 1993 to 2026.Frontiers in oncology · 2026Review
- Dental Students' and Educators' Perceptions Toward the Adoption of Artificial Intelligence in Dental Education: A Survey-Based Study.International journal of dentistry · 2026Article
- AI-assisted image analysis ofBioinformation · 2026Article
- Harnessing the power of ANN for early detection and prediction of oral cancer.Frontiers in artificial intelligence · 2026Article
- Early detection in oral cancer: Are we prepared for artificial intelligence-driven precision medicine?Annals of medicine and surgery (2012) · 2026Article
- Early detection in oral cancer: are we ready for AI-driven precision?Annals of medicine and surgery (2012) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimThe early detection of oral cancer (OC) at the earliest stage significantly increases survival rates. Recently, there has been an increasing interest in the use of artificial intelligence (AI) technologies in diagnostic medicine. This study aimed to critically analyse the available evidence concerning the utility of AI in the diagnosis of OC. Special consideration was given to the diagnostic accuracy of AI and its ability to identify the early stages of OC. MATERIALS AND
methodsFrom the date of inception to December 2021, 4 databases (PubMed, Scopus, EBSCO, and OVID) were searched. Three independent authors selected studies on the basis of strict inclusion criteria. The risk of bias and applicability were assessed using the prediction model risk of bias assessment tool. Of the 606 initial records, 17 studies with a total of 7245 patients and 69,425 images were included. Ten statistical methods were used to assess AI performance in the included studies. Six studies used supervised machine learning, whilst 11 used deep learning. The results of deep learning ranged with an accuracy of 81% to 99.7%, sensitivity 79% to 98.75%, specificity 82% to 100%, and area under the curve (AUC) 79% to 99.5%.
resultsResults obtained from supervised machine learning demonstrated an accuracy ranging from 43.5% to 100%, sensitivity of 94% to 100%, specificity 16% to 100%, and AUC of 93%.
conclusionsThere is no clear consensus regarding the best AI method for OC detection. AI is a valuable diagnostic tool that represents a large evolutionary leap in the detection of OC in its early stages. Based on the evidence, deep learning, such as a deep convolutional neural network, is more accurate in the early detection of OC compared to supervised machine learning.
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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.