From pixels to prognosis: Application and challenges of artificial intelligence in oral disease diagnosis

Abstract

Background: Early and accurate diagnosis of oral diseases is essential for effective treatment, but traditional methods can be subjective and time-consuming. Artificial intelligence (AI) offers potential improvements. This review examines the applications and challenges of AI in oral disease diagnosis. Objective: The objective of this study was to comprehensively review the applications of AI and machine learning (ML) in oral disease diagnosis, focusing on algorithms, diverse applications, and challenges, particularly within the Indian healthcare context. Method: An electronic search was conducted using the PubMed database, focusing on peer-reviewed articles published up to February 2025, using the keywords “Artificial Intelligence,” “Machine Learning,” and “Oral Disease Diagnosis,” which resulted in 15,554, 12,661, and 6,867 items, respectively. After a thorough review, relevant data from other electronic sources were also included for additional information. Result: AI algorithms show potential in enhancing diagnostic accuracy and efficiency for conditions such as caries, periodontal disease, and oral cancer. Implementation challenges include data privacy, limited datasets, regulatory compliance, interpretability, clinical workflow integration, and cost, which are amplified in the diverse Indian landscape due to disparities in healthcare access, digital infrastructure, and linguistic diversity. Conclusion: Addressing challenges through collaboration between researchers, clinicians, policymakers, and industry is essential to responsibly harnessing AI’s transformative potential for more accurate, personalized, and accessible oral healthcare.

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