ReviewCureus2025
Role of Artificial Intelligence in the Diagnosis of Oral Squamous Cell Carcinoma: A Systematic Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis 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
6 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Artificial intelligence for the detection and diagnosis of oral and maxillofacial lesions: evidence, limitations, and future directions.Brazilian oral research · 2026Review
- Deep learning-based artificial intelligence models predict survival in patients with oral cavity squamous cell carcinoma.Scientific reports · 2025Article
- Review
- Review
- Advanced drug delivery systems for oral squamous cell carcinoma: a comprehensive review of nanotechnology-based and other innovative approaches.Frontiers in drug delivery · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Oral squamous cell carcinoma (OSCC) is a serious worldwide health issue. Early OSCC identification by the analysis of digital oral photos is possible with the combination of artificial intelligence (AI) and computer vision. The purpose of this systematic review was to evaluate the current evidence on the role of AI in the diagnosis of OSCC, focusing on the diagnostic performance, methodologies employed, and potential limitations of AI applications in this context. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search for relevant studies across PubMed, Scopus, Web of Science, and Cumulative Index to Nursing and Allied Health Literature (CINAHL). In these databases, we found 286 studies, which were first screened for duplicates and then assessed on inclusion and exclusion criteria. Only 11 studies were found most relevant and were included in this study. These studies were also assessed for risk of bias using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Numerous studies have shown impressive results for this job, frequently covering about 1000 photos and regularly reaching sensitivity rates above 85% with accuracy rates above 90%. The review examines these research in detail, providing insight into their methods, which include the application of contemporary machine learning and pattern recognition techniques in conjunction with various supervision techniques. However, because various datasets are utilized in different articles, it can be difficult to compare the results. In light of these results, this study emphasizes how urgently the area of OSCC detection needs more solid and trustworthy datasets. Additionally, it emphasizes how sophisticated methods like ensemble learning, multi-task learning, and attention mechanisms can be used as essential instruments to improve the sensitivity and accuracy of OSCC identification in oral photos. Together, these observations highlight how AI-driven methods for early OSCC diagnosis have the potential to greatly enhance patient outcomes and medical procedures.
Indexed as
Identifiers
What OpenQuestion holds
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.