Evidence map›Paper›PMID 41846817›Full record

ArticleJournal of pharmacy & bioallied sciences2025

Evaluation of the Role of Artificial Intelligence-Assisted Diagnosis of Early-Stage Oral Cancer: A Clinical Study.

Sanjay K Sahoo, Meenal Jethlia, Jagjeewan Ram, Mudita Chaturvedi, Harveen Kaur, Pankaj Kukreja, M C Prashant, Rahul Tiwari

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In one paragraph

Article in Journal of pharmacy & bioallied sciences, 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
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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

8 authors.

Sanjay K SahooDepartment of Periodontics, Kalinga Institute of Dental Sciences, KIIT Deemed to be University, Bhubaneswar, Odisha, India.
Meenal JethliaDepartment of Oral Medicine and Radiology, Government Medical College, Parbhani, Maharashtra, India.
Jagjeewan RamDepartment of Pathology, ASMC Kanpur Dehat, Uttar Pradesh, India.
Mudita ChaturvediDepartment of Oral and Maxillofacial Pathology, Dr. D. Y. Patil Dental College and Hospital, Dr. D. Y. Patil Vidyapeeth, Pimpri, Pune, Maharashtra, India.
Harveen KaurDepartment of Oral Medicine and Radiology, Dr. Harvansh Singh Judge Institute of Dental Sciences and Hospital, Punjab University, Chandigarh, India.
Pankaj KukrejaDepartment of Biomedical Dental Sciences, Faculty of Dentistry, Al-Baha University, Al-Aqiq Campus, Kingdom of Saudi Arabia.
M C PrashantDepartment of Oral and Maxillofacial Surgery, RKDF Dental College and Research Centre, Bhopal, Madhya Pradesh, India.
Rahul TiwariDepartment of Oral and Maxillofacial Surgery, RKDF Dental College and Research Centre, Sarvepalli Radhakrishnan University, Bhopal, Madhya Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Early detection of oral cancer significantly enhances treatment outcomes and survival rates. This research evaluates the role of artificial intelligence (AI)-assisted diagnosis in identifying early-stage oral cancer. Methods: A clinical research involving 100 patients with suspected oral lesions was conducted. Participants underwent conventional visual examination and AI-assisted diagnostic imaging. Diagnostic accuracy, sensitivity, and specificity were compared. Results: AI-assisted diagnostics demonstrated a sensitivity of 92% and specificity of 88%, significantly outperforming conventional methods. Time to diagnosis was reduced by 40%. Conclusion: AI-assisted diagnosis is a promising tool for early-stage oral cancer detection, improving diagnostic accuracy and reducing time to intervention. Further research is recommended to validate these findings in broader clinical settings.

Indexed as

Artificial intelligenceclinical researchdiagnostic accuracyearly diagnosisoral cancer

Identifiers

PMID41846817
PMCPMC12991465

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