ArticleBMC oral health2026
Evaluating different large language model performances in evidence-based dental implantology: a blinded controlled experimental evaluation using expert panel assessment.
Article in BMC oral health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundArtificial intelligence (AI) and large language models (LLMs) are increasingly being explored for clinical decision-making and evidence-based information retrieval in dental implantology. However, concerns remain regarding the accuracy, reliability, and clinical applicability of different AI approaches, including general-purpose, fine-tuned, and retrieval-augmented generation (RAG) models. This study aimed to compare their ability to provide accurate, evidence-based, and clinically relevant responses to implantology-related questions.
methodsSixteen master's-level clinical questions were presented to all four LLMs under controlled conditions. Their anonymized responses were independently evaluated by three dental implantology specialists based on three core criteria: specificity, accuracy, and clinical utility, using a 0-10 scale. Descriptive statistics were reported as medians and interquartile ranges. Differences among models were assessed primarily using Kruskal-Wallis tests, and inter-rater reliability was evaluated using Fleiss' kappa.
resultsLLM4 consistently achieved the highest median scores across all criteria (Specificity: median 9.0, IQR 9.0-10.0; Accuracy: median 9.5, IQR 9.0-10.0; Clinical Utility: median 10.0, IQR 9.0-10.0), significantly outperforming the other models (all p < 0.001). Cohen's d revealed large to very large effect sizes in comparisons involving LLM4, particularly against LLM2 and LLM3. LLM2 scored lowest and showed the highest variability. Inter-rater reliability was slight across all metrics (κ = 0.078-0.180), indicating moderate evaluator disagreement.
conclusionsAmong the models tested, LLaMA 3.2 + RAG demonstrated superior and more consistent clinical performance in dental implantology. While fine-tuning provided moderate benefits, real-time access to structured guidelines significantly enhanced response quality. Despite low inter-rater agreement, these findings highlight the promise of RAG models in supporting high-complexity, evidence-based clinical domains, while emphasizing the need for standardization in AI evaluation protocols.
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