Enterprise AI Analysis

Introduction: The Rise of LLMs in Healthcare

Large Language Models (LLMs), built on the Transformer architecture, are transforming Natural Language Processing (NLP) tasks. Recent advancements in computing power and data availability have led to models like OpenAI's GPT series, Google's GEMINI, and Microsoft's Copilot. In 2025, DeepSeek, a large language model from China, gained popularity for its cost-effectiveness, performance, and open-source nature.

In clinical medicine, LLMs are showing potential in medical record analysis, patient screening, and documentation support, allowing clinicians to access evidence and contextualize historical data for improved treatment outcomes. AI-powered tools are also evolving medical education paradigms. Despite these advancements, the integration into medical practice is still early, with initial evaluations focusing on licensing exams and basic queries. Concerns about model inaccuracies ("hallucinations") are being addressed, leading to more complex clinical applications.

Dentistry offers a unique context for LLM integration, with structured outpatient data, extended patient-dentist interactions, and standardized diagnostic criteria. This makes it an ideal testing ground for AI-assisted decision support. However, current dental LLM studies often overlook complex data integration like medical histories and treatment protocols, crucial for periodontal management where inadequate information synthesis contributes to many errors. Our study specifically addresses this gap by evaluating LLMs' ability to interpret longitudinal dental case narratives, a critical skill for clinical reasoning, and aims to establish LLMs as simulation platforms for training dental students and enhancing real-world clinical workflows.

Results: DeepSeek V3's Performance Metrics

Our evaluation began with an analysis of MELD scores, which were consistently low across all models (GPT: 0.26, DeepSeek: 0.26, Gemini: 0.28, Copilot: 0.25). This suggests a negligible likelihood of data contamination, indicating that responses were generated from learned knowledge rather than memorized data.

Automated metrics revealed significant differences among models. For faithfulness, DeepSeek (median = 0.528) significantly outperformed GPT (0.402), Gemini (0.457), and Copilot (0.367). In answer relevancy, all models achieved high median scores near 0.95. DeepSeek maintained the highest mean relevancy, while Gemini showed a notable discrepancy with about one-third of its responses scoring below 0.5, suggesting it might produce factually consistent but irrelevant content. For readability (Flesch-Kincaid grade level), Copilot ranked highest (median = 11.9), followed by DeepSeek (12.8), GPT (12.9), and Gemini (13.1).

Expert evaluations further solidified these findings. Inter-rater reliability was strong (weighted kappa coefficient of 0.784). DeepSeek (median = 4.5) was the highest-performing model for dental queries, significantly outperforming GPT, Gemini, and Copilot (all medians = 4.0). All models exhibited clinically acceptable performance with mean scores exceeding 3.5 (70/100), confirming their non-inferiority to reference answers.

Discussion: DeepSeek's Superiority & Future Directions

This study provides a comprehensive comparative analysis of four state-of-the-art LLMs in dental case analysis. Our findings consistently show DeepSeek's superior performance over GPT-4o, Gemini 2.0 Flash, and Copilot, achieving the highest faithfulness scores and most favorable expert ratings. This suggests DeepSeek can serve as an effective adjunct to human expertise, supporting clinical education and practice.

DeepSeek's outperformance in faithfulness, a key metric for factual consistency and minimal hallucinations, was mirrored by human assessments. While DeepSeek also maintained high answer relevancy, Gemini's pattern of generating factually consistent but irrelevant content highlighted the need for careful double-checking in complex medical inquiries. Copilot excelled in readability, offering the most accessible outputs, with DeepSeek achieving second place, demonstrating that comprehensive content doesn't need to compromise clarity.

DeepSeek's advantage is attributed to its mixture-of-experts (MoE) architecture, which allows dynamic query routing to specialized neural sub-networks, enhancing domain-specific knowledge, precision, and clinical relevance. Its open-source nature further supports its potential for architectural optimization and broader clinical applications.

Limitations of the study include author-defined reference answers, which could introduce bias for ambiguous questions, and the exclusion of image data due to technical constraints. Future work will focus on establishing consensus-based gold standards through multi-expert validation and integrating image inputs. Fine-tuning experiments on GPT-4o showed improved faithfulness but slightly decreased expert scores, indicating that optimization for conciseness and tone might impact other aspects. Moving forward, the goal is to develop larger domain-specific datasets and specialized medical language models built on open-source foundations like DeepSeek.

Conclusion: DeepSeek as an Optimal AI for Dentistry

In this study, we demonstrated that large language models (LLMs) are well suited for answering dental case-vignette questions. Of the four widely recognized LLMs we evaluated, DeepSeek outperformed the others, delivering superior results and establishing itself as the optimal choice for this task. Our findings support integrating LLMs into clinical practice to boost efficiency and accessibility without compromising accuracy. Going forward, we recommend prioritizing the creation of domain-specific LLMs trained on extensive literature and case-based datasets. Such tailored models could further enhance precision, conciseness, and clinical relevance, thereby accelerating the adoption of AI-driven solutions in medicine.