Current Applications of Chatbots Powered by Large Language Models in Oral and Maxillofacial Surgery: A Systematic Review

Abstract

<b>Background/Objectives:</b> In recent years, interest has grown in the clinical applications of artificial intelligence (AI)-based chatbots powered by large language models (LLMs) in oral and maxillofacial surgery (OMFS). However, there are conflicting opinions regarding the accuracy and reliability of the information they provide, raising questions about their potential role as support tools for both clinicians and patients. This systematic review aims to analyze the current literature on the use of conversational agents powered by LLMs in the field of OMFS. <b>Methods</b>: The review was conducted following PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions. Original studies published between 2023 and 2024 in peer-reviewed English-language journals were included. Sources were identified through major electronic databases, including PubMed, Scopus, Google Scholar, and Web of Science. The risk of bias in the included studies was assessed using the ROBINS-I tool, which evaluates potential bias in study design and conduct. <b>Results</b>: A total of 49 articles were identified, of which 4 met the inclusion criteria. One study showed that ChatGPT provided the most accurate responses compared to Microsoft Copilot (ex-Bing) and Google Gemini (ex-Bard) for questions related to OMFS. Other studies highlighted that ChatGPT-4 can assist surgeons with quick and relevant information, though responses may vary depending on the quality of the questions. <b>Conclusions</b>: Chatbots powered by LLMs can enhance efficiency and decision-making in OMFS routine clinical cases. However, based on the limited number of studies included in this review (four), their performance remains constrained in complex clinical scenarios and in managing emotionally sensitive patient interactions. Further research on clinical validation, prompt formulation, and ethical oversight is essential to safely integrating LLM technologies into OMFS practices.

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