ARTIFICIAL INTELLIGENCE VS CLINICAL EXPERTISE: A COMPARATIVE ANALYSIS OF DIAGNOSTIC EFFECTIVENESS IN MODERN DENTISTRY

Ravshanbekova Xonzoda, Nosirova Maftuna,Tulanbayev Fayzullo

Authors

Ravshanbekova Xonzoda, Nosirova Maftuna,Tulanbayev Fayzullo Assistant of the Department of Therapeutic Dentistry, Kimyo International University

Keywords:

artificial intelligence, dentistry, diagnostic accuracy, comparative effectiveness, deep learning, convolutional neural networks, clinical validation.

Abstract

This article presents a critical analysis of the current state of research comparing the diagnostic effectiveness of artificial intelligence (AI) systems and dental practitioners. Based on data from systematic reviews, meta-analyses, and original clinical studies published between 2024 and 2026, the achievements and limitations of AI are examined in areas such as cariology, oral mucosal pathology, periodontology, and orthodontics. Particular attention is given to the issue of external validity in existing studies, differences in AI performance depending on the clinician’s experience, and the depth of pathological processes. The study concludes that AI should be considered as a clinical decision support tool rather than a replacement for dental professionals.

References

Kwiatek J, Leśna M, Piskórz W, Kaczewiak J. Comparison of the Diagnostic Accuracy of an AI-Based System for Dental Caries Detection and Clinical Evaluation Conducted by Dentists. Journal of Clinical Medicine. 2025;14(5):1566.

Saikia A, et al. Artificial Intelligence in dentistry: an overview of systematic reviews and meta-analysis. Evid Based Dent. 2025. DOI: 10.1038/s41432-025-10160-8

Sowmya V, Reddy GS, Sharma A, et al. Comparison of artificial intelligence vs. junior dentists’ diagnostic performance based on caries and periapical infection detection on panoramic images. African Journal of Biological Sciences. 2024;6(12):4561-4565.

Chen YJ, et al. Auxiliary evaluation of marginal ridge discrepancy in periodontal disease using deep learning on periapical radiographs. Journal of Dental Sciences. 2025. DOI: 10.1016/j.jds.2025.09.015

Shah K, Dhimole A, Pandey N, et al. Evaluation of Artificial Intelligence-Based Diagnostic Tools for Accurate Detection and Classification of Oral Lesions: A Clinical Study. Journal of Pharmacy and Bioallied Sciences. 2025;17(Suppl 4):S3138-S3140.

Araidy S, Batshon G, Mirochnik R. Artificial Intelligence Applications in Dentistry: A Systematic Review. Oral — Health, Diseases, Therapies, and Technologies. 2025;5(4):90.

Oranger A, et al. Diagnostic Support in Dentistry Through Artificial Intelligence: A Systematic Review. Bioengineering. 2025;12(11):1244.

Anonymous. Evaluating AI diagnostic accuracy in approximal dental caries detection on bitewing radiographs. Clinical Oral Investigations. 2026;30:207.

Rana A, Akhtar N. A Hybrid Deep Learning and Anomaly Detection Model for Interpretable Analysis of Dental OPG. European Journal of Prosthodontics and Restorative Dentistry. 2026;34(1):25-34.

de Lima Pereira SA, et al. Two-step pipeline for oral diseases detection and classification: a deep learning approach. Frontiers in Oral Health. 2025;6:1659323.