Vincenzo Ronsivalle, Simona Santonocito, Umberto Cammarata, Eleonora Lo Muzio, Marco Cicciù
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引用次数: 0
Abstract
Background/Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.
背景/目的：近年来，人们对基于人工智能（AI）的聊天机器人在口腔颌面外科（OMFS）中的临床应用越来越感兴趣。然而，关于它们提供的信息的准确性和可靠性存在相互矛盾的意见，这就提出了它们作为临床医生和患者支持工具的潜在作用的问题。本系统综述旨在分析当前关于在OMFS领域使用llm驱动的会话代理的文献。方法：按照PRISMA指南和Cochrane干预措施系统评价手册进行综述。研究纳入了2023年至2024年在同行评议的英语期刊上发表的原创研究。来源是通过主要的电子数据库确定的，包括PubMed、Scopus、b谷歌Scholar和Web of Science。使用ROBINS-I工具评估纳入研究的偏倚风险，该工具评估研究设计和实施中的潜在偏倚。结果：共纳入49篇文献，其中4篇符合纳入标准。一项研究表明，与Microsoft Copilot（前bing）和谷歌Gemini（前bard）相比，ChatGPT在回答与OMFS相关的问题时提供了最准确的答案。其他研究强调，ChatGPT-4可以帮助外科医生快速获得相关信息，尽管回答可能因问题的质量而异。结论：llm驱动的聊天机器人可以提高OMFS常规临床病例的效率和决策能力。然而，基于本综述中包含的有限数量的研究（四项），他们的表现在复杂的临床场景和管理情绪敏感的患者互动方面仍然受到限制。进一步研究临床验证、快速配方和伦理监督对于将法学硕士技术安全地整合到OMFS实践中至关重要。
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