ReviewClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2026
Application of artificial intelligence in oral potentially malignant disorders: current opinions and future barriers.
Review in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Enhancing the therapeutic efficacy of tumor-treating fields (TTFields): further perspectives.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 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
7 authors.
Funding
Abstract
Oral potentially malignant disorders (OPMDs) refer to oral mucosal disorders with an increased risk of malignancy, primarily oral squamous cell carcinoma (OSCC), especially in South and Southeast Asia. Since not all patients with OPMDs develop oral cancer, accurate early detection and diagnosis of malignant transformation are critically important for clinicians to determine the optimal therapeutic approach. Therefore, distinguishing OPMDs from early-stage OSCC is an increasing challenge in the clinic. Artificial intelligence (AI) technology has recently been shown to quickly identify high-risk conditions/lesions for screening oral cancer early. Moreover, the AI algorithm can also be used to determine the prognosis of OPMDs. In this review, we systematically summarize the medical records, oral images, pathological examinations, biomarkers, omics data and other aspects of the main outcomes of AI applied to address OPMDs-related issues. Furthermore, we discuss automated diagnostic systems and risk prediction tools for malignant transformation with pleasant outcomes and the potential to ultimately assist clinicians. Finally, we introduce the current challenges and barriers to AI in OPMDs on the premise that more advanced AI models and larger datasets will lead to the use of AI models in OPMDs.
Indexed as
Identifiers
40885838What 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.