Publication2025
Diagnostic Accuracy of Artificial Intelligence in Detecting Oral Potentially Malignant Disorders and Oral Cancer: A Meta-Analysis of Imaging-Based Studies (2015–2024)
The record
- Type
- Publication
- Date
- 1 January 2025
- Level
- International — held abroad
- Journal
- Journal of International Society of Preventive and Community Dentistry
- Volume
- 15
- Issue
- 5
- Pages
- 391-401
- Published
- 1 January 2025
- DOI
- 10.4103/jispcd.jispcd_116_25
- Scopus ID
- 105020061681
- ISSN
- 22310762
- Authors
- 6
- Open access
- Yes
- Citations
- 2
- International co-authorship
- Yes
- Co-author countries
- IN, NP
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
3.4 Reduce premature mortality from non-communicable diseases and promote mental health and well-being
Meta-analysis of 35 AI-assisted imaging studies demonstrates 0.919 sensitivity and 0.879 specificity for early detection of oral potentially malignant disorders and oral cancer in low-resource settings
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Synthesizes advances in convolutional neural network methods for scaled diagnostic screening systems in oral cancer detection
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
17.6 Enhance North-South, South-South and triangular cooperation on science, technology and innovation, and knowledge sharing
Structural signal: co-authorship outside India.
- Rule-derived
- Emitted by a deterministic rule from structural signals rather than judged from the record’s text. The rule carries a version, so rule-derived and judged labels stay separable.
- Produced by
- sdg17-structural-v1, under kortex-publication-v1
Viksit Bharat @2047
vb-1.2 Health and well-being
AI-enabled early detection of oral cancer significantly improves health outcomes and survival rates for affected populations
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- 1
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Citing this record
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KIIT Deemed to be University. “Diagnostic Accuracy of Artificial Intelligence in Detecting Oral Potentially Malignant Disorders and Oral Cancer: A Meta-Analysis of Imaging-Based Studies (2015–2024)”. Kortex.