Enterprise AI Analysis
Artificial intelligence performance in answering multiple-choice oral pathology questions: a comparative analysis
This report analyzes the performance of various Large Language Models (LLMs) in answering multiple-choice oral pathology questions, drawing insights from the study by Yilmaz et al. (2025). We explore the implications for AI integration in dental education and clinical decision support.
Executive Impact: Key Metrics
The study reveals significant variability in LLM performance, with ChatGPT 01 demonstrating superior accuracy in oral pathology questions. While LLMs show promise as supplementary educational tools, further validation is crucial before widespread clinical adoption. Key findings highlight differences in handling case-based versus knowledge-based questions.
96%
Highest Accuracy Achieved (ChatGPT 01)
61%
Lowest Accuracy (Copilot)
8
LLMs Evaluated
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This section details the overall accuracy rates across all 100 oral pathology questions. It highlights the top-performing and lowest-performing LLMs and the statistical significance of their differences.
Here, the performance is broken down by question type: case-based (clinical scenarios) and knowledge-based (theoretical information). This reveals how different LLMs handle practical application versus factual recall.
This part examines LLM accuracy across specific oral pathology topics, such as Odontogenic Cysts, Tumors, Mucosal Diseases, and Salivary Gland Pathology. It identifies areas where certain LLMs excel or struggle.
96%
ChatGPT 01's Leading Accuracy
Enterprise Process Flow
DUS Questions Selection (2012-2021)
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Question Categorization (Case/Knowledge-based)
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LLM Input & Response Collection
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Accuracy Assessment (Official Keys)
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Statistical Performance Analysis
| LLM | Overall Correct (%) | Case-Based Correct (%) | Knowledge-Based Correct (%) |
|---|---|---|---|
| ChatGPT 01 |
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| Claude 3.5 |
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| Gemini 2 |
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| Deepseek |
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| Copilot |
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ChatGPT 01: Leading the Pack in Oral Pathology
ChatGPT 01 consistently outperformed other LLMs in answering DUS oral pathology questions, achieving 96% overall accuracy. This suggests its extensive training dataset and advanced model architecture provide a more comprehensive knowledge base and text understanding for complex medical-dental terminology. It particularly excelled in both case-based and knowledge-based questions, demonstrating robust reasoning capabilities for clinical scenarios and strong recall of theoretical information. This leadership positions ChatGPT 01 as a strong candidate for future integration into dental education and diagnostic support systems.
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Your AI Implementation Roadmap
A strategic phased approach for integrating AI solutions, tailored to enterprise needs.
Phase 1: Pilot Integration & Training (1-3 Months)
Integrate selected LLMs (e.g., ChatGPT 01) into internal dental education platforms or diagnostic support systems for a pilot group. Develop custom training modules and fine-tuning datasets using institutional oral pathology cases to enhance domain-specific accuracy and reduce hallucination.
Phase 2: Validation & Performance Benchmarking (3-6 Months)
Conduct rigorous internal validation studies, comparing LLM performance against human experts and established diagnostic protocols. Continuously benchmark accuracy across different question types (text-based, image-based, multi-step) and clinical scenarios. Gather feedback from users (students, clinicians) to identify areas for improvement.
Phase 3: Scaled Deployment & Continuous Improvement (6-12 Months)
Based on successful pilot and validation, scale deployment to a wider user base within the institution. Establish a feedback loop for ongoing model refinement, integrating new research and clinical data. Explore ethical considerations and develop guidelines for responsible AI use in dental education and practice.
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