Can Large Language Models Serve as Reliable Tools for Information in Dentistry? A Systematic Review

Abstract

Large language models (LLMs) have gained popularity among dental students for generating subject-related answers. However, their widespread use raises significant concerns about misinformation. This systematic review aims to critically evaluate studies assessing the performance of LLMs in dentistry. A comprehensive electronic search was conducted in PubMed/Medline, Scopus, Embase, Web of Science, Google Scholar, and the Saudi Digital Library to identify studies published up to September 2024. The study quality was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). A total of 2030 studies have been identified. After removing 907 duplicate records, 1123 studies remained for screening. Ultimately, 31 studies met the inclusion criteria. Approximately half of these studies were classified as “high risk,” while the remainder were classified as “low risk.” The applicability of the findings was rated as “low concern.” The primary limitations of LLMs include their inability to specify information sources and their tendency to generate fabricated citations. Based on this review, LLMs hold promise as supplementary educational tools in dentistry. Evidence indicates that students using LLMs may achieve improved academic performance compared to traditional methods. However, concerns about occasional inaccuracies and unreliable citations underscore the need for further research, integration with validated sources, and adherence to ethical guidelines. Ultimately, LLMs should be viewed as complementary tools within dental education, with careful consideration of their limitations.