What does this research mean for the field?

Large language models demonstrate high overall scientific accuracy regarding tilted dental implant systems but exhibit significant performance variability and limitations in safety-critical domains such as contraindications and complications. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This evaluation aims to assess the scientific accuracy and guideline conformity of responses from large language models in tilted implant dentistry.

April 23, 2026

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Scientific Accuracy of Large Language Models in Tilted Implant Dentistry: A Guideline-Based Comparative Evaluation

MYMehmet S. YildizIstanbul University-Cerrahpaşa MAMelek AlkapIstanbul University-Cerrahpaşa UÖUmut ÖzdalIstanbul Technical University

Key Points

This evaluation aims to assess the scientific accuracy and guideline conformity of responses from large language models in tilted implant dentistry.
Four LLMs were independently assessed using 120 guideline-based questions across eight domains.
Responses were evaluated by a multidisciplinary expert panel using a structured ordinal scoring system.
Statistical analyses identified differences in performance across models in specific domains.
High scientific accuracy scores were generally observed across all models.
Significant differences in performance were noted in the definition, contraindications, and complications domains.
DeepSeek and Gemini consistently outperformed ChatGPT and Copilot in content related to complications.

Abstract

Tilted dental implant systems are widely used in the rehabilitation of anatomically compromised jaws and are supported by international consensus guidelines. Concurrently, large language models (LLMs) are increasingly accessed as informational tools in implant dentistry; however, their scientific accuracy and adherence to guideline-based principles in advanced implant concepts remain insufficiently explored. This study evaluated the scientific accuracy, guideline conformity, and clinical consistency of responses generated by 4 LLMs regarding tilted dental implant systems. A total of 120 guideline-based questions covering 8 predefined domains (definition, indications, contraindications, advantages, surgical procedure content, prosthetic procedure content, complications, and prognosis/survival) were developed in accordance with ITI, EAO, and AAOMS consensus reports. Each question was independently submitted to ChatGPT-5.2, Copilot, DeepSeek, and Gemini, and all responses were anonymized and evaluated by a multidisciplinary expert panel using a structured ordinal scoring system. Overall, scientific accuracy scores were high across all models, with near-ceiling performance observed in domains related to indications, advantages, procedural content, and prognosis. Statistically significant between-model differences were identified in the definition (P=0.003), contraindications (P=0.006), and complications (P<0.001) domains, with DeepSeek and Gemini demonstrating consistently higher scores in complication-related content compared with ChatGPT and Copilot. Within-model analyses further revealed significant domain-dependent variability across all LLMs. Although LLMs demonstrate a strong capacity to reproduce established, guideline-based knowledge regarding tilted implant systems, limitations remain in safety-critical domains requiring nuanced clinical judgment. Accordingly, LLMs should be regarded as adjunctive educational tools rather than substitutes for expert decision-making in craniofacial implantology.

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Cite This Study

Yildiz et al. (Tue,) studied this question.

synapsesocial.com/papers/69e9b95b85696592c86ec120 https://doi.org/https://doi.org/10.1097/scs.0000000000012768

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