ReviewCancers2022
Efficacy of Artificial Intelligence-Assisted Discrimination of Oral Cancerous Lesions from Normal Mucosa Based on the Oral Mucosal Image: A Systematic Review and Meta-Analysis.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 6 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
26 citing papers in PubMed, 6 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Non-invasive detection and monitoring of oral squamous cell carcinoma and oral potentially malignant disorders: an umbrella review on diagnostic accuracy.Clinical oral investigations · 2026Pooled it
- Detection of oral potentially malignant disorders and oral cancer with white light through deep learning: a systematic review and meta-analysis.Clinical oral investigations · 2026Pooled it
- Diagnostic performance of artificial intelligence in oral squamous cell carcinoma detection: a higher-order evidence synthesis through umbrella review and meta-meta-analysis.Frontiers in dental medicine · 2026Pooled it
- Diagnosis of Oral Cancer With Deep Learning. A Comparative Test Accuracy Systematic Review.Oral diseases · 2025Pooled it
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews.Advances in therapy · 2023Pooled it
- Predictors of Lesion Detection in a 12-Year Oral Cancer Screening Program: A Cohort Study in Northern Portugal.Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology · 2026Article
- Review
- Accuracy of AI Tools in the Diagnosis of Benign, Potentially Malignant and Malignant Oral Lesions: A Pilot Study.Journal of clinical medicine · 2026Article
- Understanding and Mitigating Bias From Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.World journal of otorhinolaryngology - head and neck surgery · 2026Review
- Biopsy for Suspicious Oral Lesions: A Review From the American Head and Neck Society-Cancer Prevention Service.Head & neck · 2026Review
- Diagnostic Efficiency of Artificial Intelligence Integrated Intraoral Mobile Photographs in Identification of Oral Potentially Malignant Disorders: An Umbrella Review.International journal of dentistry · 2026Review
- Performance of large language models on image-based oral pathology questions from the Japanese National Dental Examination.Journal of dental sciences · 2026Article
- Detect pre-cancerous tongue lesions for early oral cancer diagnosis using deep learning algorithm.Scientific reports · 2025Article
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Critical review of OCT in clinical practice for the assessment of oral lesions.Frontiers in oncology · 2025Review
- Booming Artificial Intelligence in Oral Cancer Diagnosis: From Image Processing to Stage Classification.Current medicinal chemistry · 2025Article
- A Deep Learning System to Predict Epithelial Dysplasia in Oral Leukoplakia.Journal of dental research · 2024Article
- Application of Targeted Optical Coherence Tomography in Oral Cancer: A Cross-Sectional Preliminary Study.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence for cancer screening and surveillance.ESMO real world data and digital oncology · 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
3 authors at 2 institutions in 2 countries.
Funding
Abstract
The accuracy of artificial intelligence (AI)-assisted discrimination of oral cancerous lesions from normal mucosa based on mucosal images was evaluated. Two authors independently reviewed the database until June 2022. Oral mucosal disorder, as recorded by photographic images, autofluorescence, and optical coherence tomography (OCT), was compared with the reference results by histology findings. True-positive, true-negative, false-positive, and false-negative data were extracted. Seven studies were included for discriminating oral cancerous lesions from normal mucosa. The diagnostic odds ratio (DOR) of AI-assisted screening was 121.66 (95% confidence interval [CI], 29.60; 500.05). Twelve studies were included for discriminating all oral precancerous lesions from normal mucosa. The DOR of screening was 63.02 (95% CI, 40.32; 98.49). Subgroup analysis showed that OCT was more diagnostically accurate (324.33 vs. 66.81 and 27.63) and more negatively predictive (0.94 vs. 0.93 and 0.84) than photographic images and autofluorescence on the screening for all oral precancerous lesions from normal mucosa. Automated detection of oral cancerous lesions by AI would be a rapid, non-invasive diagnostic tool that could provide immediate results on the diagnostic work-up of oral cancer. This method has the potential to be used as a clinical tool for the early diagnosis of pathological lesions.
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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.