Deep Learning in Dentistry: A Systematic Review from an AI Researcher Viewpoint

BackgroundDeep learning has achieved rapid development in recent years and has been applied to various fields in dentistry. While cross-disciplinary research between artificial intelligence and dentistry is growing exponentially, most studies rely on off-the-shelf machine learning models, with only a small portion introducing technological novelty. Furthermore, tasks such as dental disease diagnosis are inherently complex, with high intra- and interobserver variability where dentists often interpret radiographs differently and offer varying subsequent treatments. However, many studies overlooked this variability, assuming no data and model uncertainty in dental tasks. Additionally, many evaluated their methods using private and small-scale datasets, making fair comparisons of their outcome metrics challenging and introducing significant predictive bias in artificial intelligence models. The goal of the current study was to examine and critically assess recent novel advances in artificial intelligence in dentistry across a wide range of dental applications. MethodsWe begin by presenting foundational concepts in artificial intelligence and adopt a unique approach by focusing on the novelty of deep learning methods. Following that, we conducted a systematic review by searching online databases (PubMed, IEEE Xplore, arXiv, and Google Scholar) for publications related to artificial intelligence, machine learning, and deep learning applications in dentistry. ResultsA total of 91 articles met the inclusion criteria, and we presented a comprehensive analysis of the studies. Moreover, we discuss the limitations of recent studies on artificial intelligence 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. ConclusionRecent 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 outcome in dentistry.