ArticleOral (Basel, Switzerland)2025
Current AI Applications and Challenges in Oral Pathology.
Article in Oral (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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Who cites it
7 citing papers in PubMed.
- Navigating the ethical and legal landscape of artificial intelligence in dentistry.Journal of advanced periodontology & implant dentistry · 2026Article
- Adaptive diagnostic reasoning framework for pathology with multimodal large language models.Communications medicine · 2026Article
- Attention-aided hybrid transformer network for oral squamous cell carcinoma classification using histopathology images.Frontiers in artificial intelligence · 2026Article
- The Expanding Role of Artificial Intelligence in Dentistry: A Cross-Specialty Chairside Perspective.Cureus · 2025Review
- Novel metaheuristic optimized latent diffusion framework for automated oral disease detection in public health screening.Scientific reports · 2025Article
- ChatGPT in Oral Pathology: Bright Promise or Diagnostic Mirage.Medicina (Kaunas, Lithuania) · 2025Article
- Federated multimodal AI for precision-equitable diabetes care.Frontiers in digital health · 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
3 authors.
Funding
Abstract
Artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) techniques such as convolutional neural networks (CNNs) and natural language processing (NLP), has shown remarkable promise in image analysis and clinical documentation in oral pathology. In order to explore the transformative potential of artificial intelligence (AI) in oral pathology, this review highlights key studies demonstrating current AI's improvement in oral pathology, such as detecting oral diseases accurately and streamlining diagnostic processes. However, several limitations, such as data quality, generalizability, legal and ethical considerations, financial constraints, and the need for paradigm shifts in practice, are critically examined. Addressing these challenges through collaborative efforts, robust validation, and strategic integration can pave the way for AI to revolutionize oral pathology, ultimately improving patient outcomes and advancing the field.
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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.