Suha Mudhish Abduljalil Ahmed
Faculty of Dentistry, Alsaeed University, Taiz

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Artificial intelligence application in dentistry: umbrella systematic review Mona Abdulrahman Abdullah Al-Hadi; Suha Mudhish Abduljalil Ahmed; Syifa Salsabila Eddy; Afrida Nurmalasari; Kaushik Sengupta; Ninuk Hariyani
Dental Journal (Majalah Kedokteran Gigi) Vol. 59 No. 3 (2026): September
Publisher : Faculty of Dental Medicine, Universitas Airlangga https://fkg.unair.ac.id/en

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/j.djmkg.v59.i3.p300-314

Abstract

Background: Artificial intelligence (AI) is a field of computer science that employs algorithms to simulate smart decisions made by humans. It has made significant advances in the dental field in recent years. Although several systematic reviews on the use of AI in dentistry have been conducted, no comprehensive analysis of the current state of AI development and performance in dental applications has been published. Purpose: This umbrella systematic review aims to explore the expanding role of AI systems in dentistry, focusing on their applications in diagnostics, clinical decision-making, and prognosis. These core domains are essential to dental care delivery, as they directly impact treatment accuracy, planning, and outcomes. By synthesizing current evidence, this review seeks to identify advances, reveal research gaps, and support the integration of AI into routine dental practice. Methods: This study was conducted pursuant to the PRISMA guidelines. A search for full-length articles used the terms “Artificial Intelligence,” “Dentistry,” “Application,” “Systematic,” and “Review.” Results: This systematic review included articles discussing dental and maxillofacial radiology, deep learning models, endodontics, periodontics, forensic odontology, dental implants, dental caries detection, cancer detection, conservative dentistry, prosthodontics, and high-quality patient care. AI models can be considered an assistant in diagnosis, treatment, and prognosis across these various dental fields. Conclusion: AI models have demonstrated potential abilities for diagnostic, therapeutic, and prognostic evaluations in dentistry. For their safe integration into routine clinical practice, it is critical to improve these models in terms of explainability, interpretability, and generalizability through external validation. Applying these models in clinical procedures entails the need to establish appropriate regulatory frameworks.