Large language models in implant dentistry: a scoping review of applications, performance, and limitations
Abstract
BACKGROUND: This scoping review evaluates the current state of generative artificial intelligence (AI) in implant dentistry, focusing on the performance, clinical applications, and inherent limitations of large language models (LLMs) in both clinical and educational settings.
METHODS: A scoping review was conducted in accordance with PRISMA-ScR guidelines. A comprehensive search across four electronic databases (PubMed, Web of Science, Scopus, and Embase) was performed for literature published through December 2025. Eighteen eligible studies were analyzed to assess model architectures, specific task performance, and comparative proficiency against human experts.
RESULTS: The included studies, all published in 2024 or 2025, were classified as examining patient interaction (n = 9), medical knowledge (n = 7), or diagnostic assessment (n = 2). The analysis revealed a distinct evolution in performance with the advent of advanced reasoning models (e.g., ChatGPT-o1, DeepSeek-R1), which occasionally surpassed licensed dentists in certification examinations. Comparative assessments between medical-specific models and general-purpose LLMs yielded divergent outcomes, indicating that domain specialization does not inherently guarantee superior clinical accuracy against state-of-the-art generalist architectures. Nevertheless, reliability remains a concern; despite the integration of retrieval-augmented generation, hallucinations persist-especially in systematic search tasks-and the…
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