ArticleBioinformation2025
AI-based diagnostic tools for oral cancer: A systematic review.
Article in Bioinformation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) as a means of enhancing and improve the early detection and diagnosis of oral cancer. While traditional diagnostic methods are effective, they are inherently subjective, not widely accessible, and often result in detection only at later stages of disease progression. The literature over the past few years has importantly identified that AI models, in particular deep learning and convolutional neural networks, displayed high diagnostic accuracy on clinical, histopathological and optical imaging data. However, challenges exist in the form of variability in the data sets, limited external validation in clinical practice and or interpretability of AI models for clinical use. In conclusion, AI presents a compelling opportunity as a supportive adjunct for oral oncology, although standardized validation and real-world implementation should occur before widespread utilization.
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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.