DeepSeek在牙科病例分析中表现优于其他大模型。
DeepSeek performs better than other Large Language Models in Dental Cases
- 对比GPT-4o等四款模型，用34个牙周病例测试推理能力。
- DeepSeek在忠实度上得分0.528，专家评分达4.5/5，居首。
- 适合医学教育与研究场景，可作专业领域辅助工具。
大语言模型（LLMs）在医疗领域具有变革潜力，但其对纵向患者病历的理解能力仍待深入探索。牙科拥有丰富的结构化临床数据，是评估LLM推理能力的理想场景。本研究评估了四种先进LLM（GPT-4o、Gemini 2.0 Flash、Copilot和DeepSeek V3）在开放性临床任务中分析34个标准化纵向牙周病例（共258个问答对）的表现。通过自动化指标与注册牙医盲评双重验证，结果显示DeepSeek表现最优，忠实度中位数达0.528（其余为0.367–0.457），专家评分中位数为4.5/5（其余为4.0/5），且阅读性无明显下降。研究证实DeepSeek是当前牙科病例分析的领先模型，支持其作为医学教育与科研的辅助工具，并展现出领域专用代理的潜力。
原文摘要 · Abstract (English)
Large language models (LLMs) hold transformative potential in healthcare, yet their capacity to interpret longitudinal patient narratives remains inadequately explored. Dentistry, with its rich repository of structured clinical data, presents a unique opportunity to rigorously assess LLMs' reasoning abilities. While several commercial LLMs already exist, DeepSeek, a model that gained significant attention earlier this year, has also joined the competition. This study evaluated four state-of-the-art LLMs (GPT-4o, Gemini 2.0 Flash, Copilot, and DeepSeek V3) on their ability to analyze longitudinal dental case vignettes through open-ended clinical tasks. Using 34 standardized longitudinal periodontal cases (comprising 258 question-answer pairs), we assessed model performance via automated metrics and blinded evaluations by licensed dentists. DeepSeek emerged as the top performer, demonstrating superior faithfulness (median score = 0.528 vs. 0.367-0.457) and higher expert ratings (median = 4.5/5 vs. 4.0/5), without significantly compromising readability. Our study positions DeepSeek as the leading LLM for case analysis, endorses its integration as an adjunct tool in both medical education and research, and highlights its potential as a domain-specific agent.
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