用GPT-5模拟糖尿病诊断，验证其临床与患者双用途潜力
LLM-Based Support for Diabetes Diagnosis: Opportunities, Scenarios, and Challenges with GPT-5
- 基于合成病例和ADA标准，测试GPT-5在五类场景中的诊断能力
- 对齐率达90%以上，可生成可解释的临床推理与患者说明
- 适合医疗AI评估、临床辅助系统开发人员参考
糖尿病是全球重大健康挑战，影响超5亿成年人，未来患病率将持续上升。尽管美国糖尿病协会（ADA）制定了明确诊断标准，但早期识别仍因症状模糊、检验值临界、妊娠期复杂性及长期监测需求而困难。大语言模型（LLMs）的发展为生成结构化、可解释且患者友好的决策支持提供了可能。本研究通过完全基于合成病例的仿真框架，评估最新生成式预训练模型GPT-5的表现，该框架依据ADA 2025标准，并借鉴NHANES、Pima Indians、EyePACS、MIMIC-IV等公开数据集构建。测试了五类典型场景：症状识别、实验室结果解读、妊娠期糖尿病筛查、远程监测及多模态并发症检测。GPT-5对每类案例完成分类、生成临床推理、输出患者可理解说明，并生成结构化JSON摘要。结果表明其判断与ADA标准高度一致，提示GPT-5可作为医生与患者共用的双重工具，同时强调建立可复现评估框架对负责任地评估医疗领域LLMs的重要性。
原文摘要 · Abstract (English)
Diabetes mellitus is a major global health challenge, affecting over half a billion adults worldwide with prevalence projected to rise. Although the American Diabetes Association (ADA) provides clear diagnostic thresholds, early recognition remains difficult due to vague symptoms, borderline laboratory values, gestational complexity, and the demands of long-term monitoring. Advances in large language models (LLMs) offer opportunities to enhance decision support through structured, interpretable, and patient-friendly outputs. This study evaluates GPT-5, the latest generative pre-trained transformer, using a simulation framework built entirely on synthetic cases aligned with ADA Standards of Care 2025 and inspired by public datasets including NHANES, Pima Indians, EyePACS, and MIMIC-IV. Five representative scenarios were tested: symptom recognition, laboratory interpretation, gestational diabetes screening, remote monitoring, and multimodal complication detection. For each, GPT-5 classified cases, generated clinical rationales, produced patient explanations, and output structured JSON summaries. Results showed strong alignment with ADA-defined criteria, suggesting GPT-5 may function as a dual-purpose tool for clinicians and patients, while underscoring the importance of reproducible evaluation frameworks for responsibly assessing LLMs in healthcare.
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