Enterprise AI Analysis
A comparative analysis of the performance of large Language models in the dentistry specialty examination
Authored by Gediz Geduk, Utku Cem Hasırcı, Didem Dumanlı Kusay, Rabia Çayır Aras, İsmail Çapar, Edanur Altın & Çiğdem Şeker
This study evaluates the accuracy and reliability of LLM-based chatbots using Dentistry Specialization Entrance Examination (DUS) questions from Türkiye to assess dental graduates' knowledge. Findings highlight the significant potential of LLMs in dental education and clinical support, while also identifying areas for improvement, particularly in visual interpretation.
Executive Impact: Key Performance Indicators
Understanding the core performance metrics of LLMs in specialized dental examinations reveals their current capabilities and areas for strategic deployment.
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Peak LLM Accuracy
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Highest Image-Based Accuracy
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Accuracy in Oral Pathology
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
LLM Performance Across All Questions
ChatGPT 4.0 consistently demonstrated superior accuracy across the entire dataset, setting a benchmark for other models. While some models like Copilot and Gemini showed competitive results, their error rates were notably higher when compared against questions ChatGPT 4.0 answered correctly, indicating less consistent performance.
| Model | Correct (n) / Percentage(%) | Incorrect (n) / Percentage(%) |
|---|---|---|
| ChatGPT 4.0 | 190 / 91.3% | 18 / 8.7% |
| Copilot | 181 / 87% | 27 / 13% |
| Gemini | 179 / 86.1% | 29 / 13.9% |
| ChatGPT 4.5 | 171 / 82.2% | 37 / 17.8% |
| DeepSeek | 171 / 82.2% | 37 / 17.8% |
| Claude | 163 / 78.4% | 45 / 21.6% |
| Grok | 157 / 75.5% | 51 / 24.5% |
Image-Based Question Performance
A significant performance gap was observed in image-based questions compared to text-based ones. While top models like ChatGPT 4.0, Gemini, and Copilot managed 63.6% accuracy, DeepSeek lagged considerably at 36.4%, highlighting a critical area for development in visual AI interpretation within medical contexts.
| Model | Image Based Correct (%) | Text Based Correct (%) | P value |
|---|---|---|---|
| ChatGPT 4.0 | 63.6% | 96.3% | 0.009 |
| Gemini | 63.6% | 87.3% | 0.50 |
| Copilot | 63.6% | 88.3% | 0.039 |
| Claude | 54.5% | 79.7% | 0.063 |
| ChatGPT 4.5 | 54.5% | 83.8% | 0.028 |
| Grok | 45.5% | 77.2% | 0.028 |
| DeepSeek | 36.4% | 84.8% | 0.001 |
Enterprise Process Flow
208 DUS Questions Selected (Text/Image)
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Questions Submitted to 7 LLMs (Turkish)
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Responses Systematically Recorded & Evaluated
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Accuracy Compared to Pre-established Answer Key
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Statistical Analysis (Chi-square, ANOVA, Tukey)
The study utilized a rigorous methodology to ensure consistent and fair evaluation of LLM performance. Questions were drawn from publicly available dental specialization exams in Turkey, carefully categorized, and submitted to each model in a controlled environment to minimize bias.
Strategic Implications & Future Outlook
LLMs as Complementary Tools for Dental Education
The study confirms that next-generation LLMs, particularly ChatGPT 4.0, Gemini, and Copilot, offer significant potential for dental education and clinical decision-making. Their strong performance on multiple-choice questions suggests they can enhance self-directed learning by providing rapid explanations and supplementary resources. However, their limitations, especially in visual interpretation and specific clinical reasoning tasks, underscore the need for human oversight.
For enterprises, this implies that LLMs are powerful assets when integrated as supportive tools rather than standalone decision-makers. Continuous evaluation against expert-validated datasets and improvements in explainability are crucial for their reliable application in high-stakes environments like healthcare.
Key takeaway: LLMs are not replacements for human experts but powerful augmentations that can streamline processes and improve access to information when deployed strategically.
Calculate Your Potential AI ROI
Estimate the time savings and financial benefits your organization could realize by integrating AI for knowledge-based tasks, inspired by the efficiencies demonstrated in this dental education study.
Annual Cost Savings
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A phased approach ensures successful integration and maximum benefit from large language models in specialized educational or clinical settings.
Phase 1: Needs Assessment & Pilot Study
Identify specific knowledge-based tasks in your organization (e.g., patient education, preliminary diagnosis support) where LLMs can augment existing workflows. Conduct a small-scale pilot using a top-performing model like ChatGPT 4.0 on a curated dataset to validate its accuracy and utility in your context.
Phase 2: Customization & Fine-tuning
Based on pilot results, identify areas requiring improved accuracy or specialized knowledge. Explore fine-tuning LLMs with proprietary organizational data or integrating them with vision-based AI for enhanced image interpretation capabilities, addressing identified gaps.
Phase 3: Integration & Training
Seamlessly integrate the AI solution into existing educational platforms or clinical decision support systems. Develop comprehensive training programs for staff and users, emphasizing LLMs as complementary tools that require human oversight and critical evaluation of outputs.
Phase 4: Continuous Monitoring & Iteration
Establish robust monitoring frameworks to track LLM performance, user feedback, and ethical compliance. Regularly update models and refine integration strategies based on new data and evolving organizational needs, ensuring long-term value and reliability.
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