Artificial Intelligence in Dentistry: A Systematic Review

Author(s):Adv. Dr. Mohammed Abdul Kabir Shaikh, Dr. Osman Abdul Kabir Shaikh, Dr. Nisa Najeeb Chawre, Dr. Mannu Batra, Dr. Deeksha Gijwani, Dr. Alman Godil, Dr. Imran Warind, Adv. Dr. Pavan Mahesh Shah, Master Arsh Shaikh, Dr. Omer Abdul Kabir Shaikh

Received: 2026-09-02 Accepted: 2026-09-17 Published: 2026-09-24

Abstract

Background: Artificial Intelligence (AI) represents a transformative force in healthcare and dentistry, with potential to improve disease detection, treatment planning, health education, and population-level oral health outcomes. While AI applications in clinical dentistry are increasingly documented, the role of AI specifically in public health dentistry—the discipline concerned with oral health promotion, disease prevention, and population-based care—remains less systematically explored.
Objectives: This systematic review aims to critically evaluate the literature on AI applications in public health dentistry, describing key domains of use, outcomes, challenges, ethical considerations, and future directions.
Methods: A comprehensive search was conducted across multiple databases (e.g., PubMed, Web of Science, Scopus) using predefined terms including “artificial intelligence,” “public health dentistry,” “AI in dental public health,” and related keywords. Studies published up to 2025 were included if they reported original research, reviews, or systematic analyses focusing on AI within the context of public health dentistry. Articles were assessed for relevance, methodology, outcomes, and implications.
Results: Twenty-five articles were identified that addressed AI’s role in public health dentistry and related domains (e.g., education, epidemiology, telehealth). Key application areas included predictive risk modeling for oral diseases, AI-assisted epidemiological surveillance, patient education and engagement through chatbots/virtual assistants, integration of AI in teledentistry, and enhancement of dental public health research and academic writing. AI models improved diagnostic accuracy for population-level screening, supported efficient data management, and facilitated personalized health messaging. Challenges identified included concerns around data bias, privacy, ethical use, and integration into existing public health systems.
Conclusions: AI applications hold promising potential to improve public health dentistry outcomes by enhancing prevention, diagnosis, education, and system efficiency. However, ethical frameworks, robust evaluation in diverse real-world populations, and clinician oversight remain essential. Continued research must address implementation barriers and ensure equitable benefits across communities.

Keywords: artificial intelligence, machine learning, public health dentistry, oral epidemiology, and teledentistry.

DOI: 10.5281/zenodo.22937962

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