Evidence map›Paper›PMID 41312916›Full record

SynthesisAsian Pacific journal of cancer prevention : APJCP2025

Artificial Intelligence Innovations in Understanding Oral Submucous Fibrosis: A Nouvelle Modernistic Approach: A Systematic Review.

Ankita Bohra, T N Uma Maheswari, Aditya Harsh

Abstract readSystematic Review
In one paragraph

Synthesis in Asian Pacific journal of cancer prevention : APJCP, 2025. 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

3 authors.

Ankita Bohra17E 748 Chopasni Housing Board, India.ORCID 0000-0002-2729-0963
T N Uma MaheswariDepartment of Oral Medicine and Radiology Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, India.ORCID 0000-0002-2366-2336
Aditya HarshOral & maxillofacial Surgeon, Head & Neck Oncosurgeon, Sarvodaya Multispeciality Hospital Faridabad, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundOral submucous fibrosis (OSMF) is a common precancerous condition prevalent in the South Asian continent. An immediate diagnosis and identification of this lesion is essential to prevent its malignant transformation. Artificial intelligence can analyze data, patient records, and clinical photographs to finalize a treatment plan in oral submucous fibrosis patients by the oral pathologist and clinicians' intervention. The current study aimed to establish the key role of machine learning software in diagnostic intervention and management of oral submucous fibrosis in the mass population with ease for most health professionals. MATERIAL &

methodsWe searched relevant databases like PubMed, Scopus, Web of Science, Cochrane, and Google Scholar using keywords like artificial intelligence, machine learning, treatment planning, diagnosis, oral submucous fibrosis, image analysis, predictive modeling, and deep neural networks.

resultsNine studies were selected after all scrutiny, with suggested outcomes under AI that can be utilized to understand oral submucous fibrosis intervention. Machine learning is a helpful tool in fabricating image prediction models under neural networks. This can be an important diagnostic aid for oral health professionals.

conclusionsIn the field of oral pathologies, AI has the potential to enhance diagnostic accuracy and patient care, improve diagnostic outcomes, and improve the prognosis of the disease. However, the development and application of AI in oral healthcare, especially for oral submucous fibrosis patients, is a new frontier for modernistic approaches for future treatment amenities.

Indexed as

Artificial IntelligenceMachine LearningOral Submucous FibrosisHumansNeural Networks, ComputerPrognosisArtificial intelligenceDiagnosisMachine LearningOral submucous fibrosisPredictive modelling

Identifiers

PMID41312916
PMCPMC12927514

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Registered trials

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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.