Deep Learning in Dentistry: A Literature Review from an AI Researcher’s Viewpoint

Abstract

Deep learning has achieved rapid development in recent years and has been applied to various fields in dentistry. While cross-disciplinary research between artificial intelligence (AI) and dentistry is growing exponentially, most studies rely on off-the-shelf machine learning models, with only a small portion introducing technological novelty. Thus, the goal of the current study is to examine and critically assess recent novel advances of deep learning in dentistry across a wide range of dental applications, from the perspective of AI researchers. We begin by presenting foundational concepts in AI and adopt a unique approach by focusing on the novelty of deep learning methods. Following that, we conducted a literature review by searching online databases (PubMed, IEEE Xplore, arXiv, and Google Scholar up to November 2024) for publications related to AI, machine learning, and deep learning applications in dentistry. 89 articles met the inclusion criteria, and we presented a comprehensive analysis of the included studies. Moreover, we discuss the limitations of recent studies on AI in dentistry and identify key research opportunities for progress and innovation. These include integrating dental domain knowledge, quantifying uncertainty, leveraging large models and multiple sources of datasets, developing efficient deep learning pipelines, and conducting thorough evaluations in both simulated and real-world experimental settings. Recent advancements in deep learning demonstrate great potential in dentistry applications. However, future research to address the limitations in recent studies is needed to fully realize its potential for enhancing dental professionals to utilize AI effectively and improve clinical and patient outcomes in dentistry.

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