Artificial intelligence in oral pathology: Friend and foe

Abstract

Objectives: To review current applications of artificial intelligence (AI) in oral pathology and critically evaluate its advantages, limitations, and future implications in diagnostic practice. Methods: A narrative review of published literature was conducted focusing on AI-based diagnostic tools, including machine learning, deep learning, convolutional neural networks, and natural language processing in oral pathology. Studies addressing clinical performance, workflow optimization, ethical concerns, and data limitations were analyzed. Results: AI demonstrated significant potential in improving diagnostic accuracy, image interpretation, and clinical documentation. Automated image analysis systems showed enhanced detection of oral lesions and streamlined reporting processes. However, challenges related to data quality, generalizability, ethical concerns, regulatory frameworks, and financial constraints were frequently reported. Conclusions: Artificial intelligence represents a powerful adjunct to oral pathology practice. Strategic integration supported by robust validation and ethical governance is essential to maximize its clinical benefits while minimizing risks.

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