Digital Convergence in Dental Informatics: A Structured Narrative Review of Artificial Intelligence, Internet of Things, Digital Twins, and Large Language Models with Security, Privacy, and Ethical Perspectives

Abstract

Background: Dentistry is undergoing a digital transformation driven by emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Digital Twins (DTs), and Large Language Models (LLMs). These advancements offer new paradigms in clinical diagnostics, patient monitoring, treatment planning, and medical education. However, integrating these technologies also raises critical questions around security, privacy, ethics, and trust. Objective: This review aims to provide a structured synthesis of the recent literature exploring AI, IoT, DTs, and LLMs in dentistry, with a specific focus on their application domains and the associated ethical, privacy, and security concerns. Methods: A comprehensive literature search was conducted across PubMed, IEEE Xplore, and SpringerLink using a custom Boolean query string targeting publications from 2020 to 2025. Articles were screened based on defined inclusion and exclusion criteria. In total, 146 peer-reviewed articles and 18 technology platforms were selected. Each article was critically evaluated and categorized by technology domain, application type, evaluation metrics, and ethical considerations. Results: AI-based diagnostic systems and LLM-driven patient support tools were the most prominent technologies, primarily applied in image analysis, decision-making, and health communication. While numerous studies reported high performance, significant methodological gaps exist in evaluation design, sample size, and real-world validation. Ethical and privacy concerns were mentioned frequently, but were substantively addressed in only a few works. Notably, IoT and Digital Twin implementations remained largely conceptual or in pilot stages, highlighting a technology gap in dental deployment. Conclusions: The review identifies significant potential for converged intelligent dental systems but also reveals gaps in integration, security, ethical frameworks, and clinical validation. Future work must prioritize cross-disciplinary development, transparency, and regulatory alignment to realize responsible and patient-centered digital transformation in dentistry.

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