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Updated: Jul 18, 2026
Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies
Published on: August 11, 2023
Diagnostic Performance of AI-Powered Histopathology: Can Machines Match Oral Pathologists in Identifying Epithelial
Nishath Sayed Abdul1, Sharad Chand2, Shishir Dhar3
1Department of OMFS and Diagnostic Sciences, Riyadh Elm University, Riyadh, Kingdom of Saudi Arabia.
Background:
Epithelial dysplasia is a critical precursor to oral squamous cell carcinoma. Manual histopathology, though gold standard, is subject to variability and delay.
Objective:
To evaluate the diagnostic accuracy of an artificial intelligence (AI)-powered histopathology system in identifying and grading oral epithelial dysplasia.
Materials And Methods:
A cross-sectional study compared AI analysis of digitized histology slides (n = 250) to manual diagnoses by expert pathologists. Agreement, sensitivity, specificity, precision, and Cohen's kappa were calculated.
Results:
AI achieved >93% diagnostic accuracy across categories. Strong agreement was noted for non-dysplastic and severe lesions; moderate discrepancies occurred in mild/moderate grades.
Conclusion:
AI demonstrates strong potential in aiding dysplasia diagnosis, particularly for high-volume screening, though human oversight remains essential.
