ReviewBiomedicines2023
Application and Performance of Artificial Intelligence (AI) in Oral Cancer Diagnosis and Prediction Using Histopathological Images: A Systematic Review.
Review in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 3 syntheses or guidelines pooled it, 61 citations in OpenAlex.
- Diagnostic performance of convolutional neural network-based AI in detecting oral squamous cell carcinoma: a systematic review and meta-analysis.BMC oral health · 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
- Diagnostic Accuracy of Artificial Intelligence Compared to Biopsy in Detecting Early Oral Squamous Cell Carcinoma: A Systematic Review and Meta Analysis.Asian Pacific journal of cancer prevention : APJCP · 2024Pooled it
- Redefining oropharyngeal cancer in the HPV era: integrating precision medicine and immunotherapeutic frontiers.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Review
- Artificial Intelligence in Oral Cancer Diagnosis: Overcoming Challenges for Enhanced Outcomes.Current medicinal chemistry · 2026Review
- Artificial intelligence and its application in early oral cancer screening: a systematic review.Frontiers in oncology · 2026Review
- Harnessing the power of ANN for early detection and prediction of oral cancer.Frontiers in artificial intelligence · 2026Article
- Deep learning-based artificial intelligence models predict survival in patients with oral cavity squamous cell carcinoma.Scientific reports · 2025Article
- Research progress on carcinogenic factors and personalized treatment of oral cancer.Discover oncology · 2025Review
- Artificial Intelligence in the Study of Root and Canal Anatomy: A Comprehensive Review on Applications, Advantages, Challenges and Future Directions.European endodontic journal · 2025Review
- Evaluation of deepseek, gemini, ChatGPT-4o, and perplexity in responding to salivary gland cancer.BMC oral health · 2025Article
- Can deepseek and ChatGPT be used in the diagnosis of oral pathologies?BMC oral health · 2025Article
- Artificial intelligence for image recognition in diagnosing oral and oropharyngeal cancer and leukoplakia.Scientific reports · 2025Article
- Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications.Diagnostics (Basel, Switzerland) · 2025Review
- Integrating support vector machines and deep learning features for oral cancer histopathology analysis.Biology methods & protocols · 2025Article
- Article
- Artificial intelligence-driven clinical decision support systems for early detection and precision therapy in oral cancer: a mini review.Frontiers in oral health · 2025Review
- Booming Artificial Intelligence in Oral Cancer Diagnosis: From Image Processing to Stage Classification.Current medicinal chemistry · 2025Article
- Embracing change, moving with time: exploring the role of digital technologies and accelerators in promoting community oral health in Africa.Frontiers in oral health · 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
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral cancer (OC) is one of the most common forms of head and neck cancer and continues to have the lowest survival rates worldwide, even with advancements in research and therapy. The prognosis of OC has not significantly improved in recent years, presenting a persistent challenge in the biomedical field. In the field of oncology, artificial intelligence (AI) has seen rapid development, with notable successes being reported in recent times. This systematic review aimed to critically appraise the available evidence regarding the utilization of AI in the diagnosis, classification, and prediction of oral cancer (OC) using histopathological images. An electronic search of several databases, including PubMed, Scopus, Embase, the Cochrane Library, Web of Science, Google Scholar, and the Saudi Digital Library, was conducted for articles published between January 2000 and January 2023. Nineteen articles that met the inclusion criteria were then subjected to critical analysis utilizing QUADAS-2, and the certainty of the evidence was assessed using the GRADE approach. AI models have been widely applied in diagnosing oral cancer, differentiating normal and malignant regions, predicting the survival of OC patients, and grading OC. The AI models used in these studies displayed an accuracy in a range from 89.47% to 100%, sensitivity from 97.76% to 99.26%, and specificity ranging from 92% to 99.42%. The models' abilities to diagnose, classify, and predict the occurrence of OC outperform existing clinical approaches. This demonstrates the potential for AI to deliver a superior level of precision and accuracy, helping pathologists significantly improve their diagnostic outcomes and reduce the probability of errors. Considering these advantages, regulatory bodies and policymakers should expedite the process of approval and marketing of these products for application in clinical scenarios.
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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.