Evidence map›Paper›PMID 42253830›Full record

ArticleJournal of dentistry (Shiraz, Iran)2026

Use of Artificial Intelligence in the Early Diagnosis of Oral Cancer: Current Update, Limitations and Future Perspectives.

Manas Bajpai

Abstract read
In one paragraph

Article in Journal of dentistry (Shiraz, Iran), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Manas BajpaiDept. of Oral Pathology and Microbiology, Rural Dental College, Loni, Maharashtra, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Oral cancer represents a significant global health challenge with rising incidence and mortality rates in various regions. The prognosis of oral cancer significantly increases if it is diagnosed in early stages. Artificial intelligence (AI) has demonstrated significant potential in enhancing early oral cancer diagnosis through various applications, revolutionizing the field of oral pathology. These innovative approaches are transforming the way healthcare professionals detect, asses and mange the cases of oral cancer cases, potentially leading to improved patient outcomes and reduced mortality rates. This brief communication explores the integration AI in the early diagnosis of oral cancer, highlighting recent advancements and potential future applications. The study reviews current methodologies employing AI technologies, such as machine learning and deep learning algorithms, to enhance the accuracy and efficiency of oral cancer detection. It discusses the role of AI in analyzing clinical images, patient data, and other diagnostic tools, emphasizing its ability to identify precancerous lesions and reduce diagnostic delays.

Indexed as

Artificial intelligenceOral cancerOral healthSquamous cell carcinoma

Identifiers

PMID42253830
PMCPMC13242053

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.