{
  "abstract": "Background\n\nArtificial 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.Methods\n\nSixteen 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.Results\n\nLLM4 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.Conclusions\n\nAmong 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.",
  "abstract_shown": true,
  "affiliations": [
    "Research Center for Digital Technologies in Dentistry and CAD/CAM, Department of Dentistry, Faculty of Medicine and Dentistry, Danube Private University, Steiner Landstrasse 124, Krems an der Donau, 3500, Austria. vasileios.alevizakos@googlemail.com.",
    "Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders, Charité-Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Aßmannshauser Str. 4- 6, Berlin, 14197, Germany. vasileios.alevizakos@googlemail.com.",
    "Department of Oral and Maxillofacial Surgery, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, Berlin, 13353, Germany.",
    "Research Center for Digital Technologies in Dentistry and CAD/CAM, Department of Dentistry, Faculty of Medicine and Dentistry, Danube Private University, Steiner Landstrasse 124, Krems an der Donau, 3500, Austria.",
    "Department of Periodontology, Endodontology and Cariology, University Center for Dental Medicine UZB, University of Base, Mattenstrasse 40, Basel, CH-4058, Switzerland."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 7,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Vasilios Alevizakos"
    },
    {
      "affs": [
        2
      ],
      "name": "Robert Gaudin"
    },
    {
      "affs": [
        2
      ],
      "name": "Iman Ghanad"
    },
    {
      "affs": [
        3
      ],
      "name": "Jonathan von See"
    },
    {
      "affs": [
        3
      ],
      "name": "Theodor von See"
    },
    {
      "affs": [
        4
      ],
      "name": "Florin Eggmann"
    },
    {
      "affs": [
        3
      ],
      "name": "Constantin von See"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8n1h8e7kqz932.bib",
  "cited_by": 0,
  "doi": "10.1186/s12903-026-09368-2",
  "fulltext_url": "https://europepmc.org/articles/PMC13679227",
  "id": "8n1h8e7kqz932",
  "issns": [
    "1472-6831"
  ],
  "landing_url": "https://europepmc.org/article/MED/42894220",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RK1-715"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7170155881",
  "pdf_url": "https://europepmc.org/articles/PMC13679227?pdf=render",
  "pmcid": "PMC13679227",
  "pmid": "42894220",
  "published": "2026-07-01",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/8n1h8e7kqz932.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:42Z",
      "native_id": "MED:42894220",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42894220"
    },
    {
      "first_seen": "2026-10-30T13:46:32Z",
      "native_id": "3c2a3437a25041a8b94315835a4f40eb",
      "source": "doaj",
      "url": "https://doi.org/10.1186/s12903-026-09368-2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Evaluating different large language model performances in evidence-based dental implantology: a blinded controlled experimental evaluation using expert panel assessment.",
  "title_text": "Evaluating different large language model performances in evidence-based dental implantology: a blinded controlled experimental evaluation using expert panel assessment.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:46:32Z",
  "url": "https://research.draconitic.com/paper/8n1h8e7kqz932",
  "venue": "BMC oral health",
  "versions": []
}