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Research Paper | Information Technology | Volume 15 Issue 8, August 2026 | Pages: 345 - 353 | India
Artificial Intelligence-Powered Early Detection of Oral Diseases: A Comparative Study of Machine Learning Models in Dental Diagnostics
Abstract: Machine learning is a subset of artificial intelligence that allows a computer system to learn patterns from data rather than follow rules written by a programmer. Together with artificial neural networks and deep learning, it offers substantial opportunity to transform diagnostics in both medicine and dentistry. Realising that opportunity, however, requires clinicians to understand what these technologies actually do, where their accuracy comes from, and where it fails. Objective. This review examines the use of artificial intelligence in the diagnosis of oral and maxillofacial disease, compares the performance of the model families that have been applied to each condition, and sets out the opportunities and the obstacles that stand between current research and routine chairside use. Methods: A structured search of PubMed, Google Scholar and ScienceDirect was carried out for studies published between January 2016 and December 2021, using paired search terms covering model architectures and disease entities. Reference lists of the selected articles were screened for additional relevant work. Results: Artificial intelligence has been applied successfully to clinical records and diagnostic images for the detection of dental caries, tooth fracture, periodontal disease, maxillary sinus pathology, salivary gland disorders, temporomandibular joint disorders, osteoporosis and oral cancer. Reported accuracy is frequently comparable to, and in several studies better than, that of experienced clinicians. Models trained on sufficiently large datasets detect micro-features that escape the human eye, support surveillance across whole populations, and help decide whether specialist referral is warranted. Conclusion: Artificial intelligence in dentistry remains predominantly research-based; despite an extensive published literature, it has not yet been absorbed into day-to-day practice. Progress now depends less on new architectures than on large, well-annotated and representative datasets, external validation, regulatory clarity and clinician training. Addressing these gaps is the route by which diagnosis and preventive oral healthcare are likely to change over the coming decade.
Keywords: artificial intelligence, artificial neural network, convolutional neural network, deep learning, machine learning, dental diagnostics, oral diseases, early detection
How to Cite?: Shikaina Gill, Raghu Raja Mehra, "Artificial Intelligence-Powered Early Detection of Oral Diseases: A Comparative Study of Machine Learning Models in Dental Diagnostics", Volume 15 Issue 8, August 2026, International Journal of Science and Research (IJSR), Pages: 345-353, https://www.ijsr.net/getabstract.php?paperid=SR26804080351, DOI: https://dx.doi.org/10.21275/SR26804080351