本地部署的小模型可精准提取慢病记录并生成高质量总结。
Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients
- 用本地运行的微型语言模型提取病历中的关键临床特征。
- 在1680次任务中平均准确率达82.25%，总结质量获医生高度评价。
- 适合需要隐私保护的慢性皮肤病长期随访场景，助医生减负。
慢性皮肤病如天疱疮需长期随访，产生大量临床记录，常规就诊中难以全面回顾，增加医生工作量且易遗漏重要历史信息。本研究评估了本地部署的隐私保护型小语言模型（SLM）从长期皮肤科随访记录中提取结构化临床特征并生成纵向摘要的可行性。在一项回顾性病例系列中，30名天疱疮患者提供541份门诊记录，累计89,336词，经两名专家标注56个临床相关特征。使用Qwen3 4B Thinking 2507模型对每份完整记录进行查询，完成56项特征提取和一份最终报告生成。在1680次特征检索任务中，平均准确率为82.25%。医生对AI生成摘要的整体质量（8.23-8.47）、临床准确性（7.93-8.20）和实用性（8.47-8.50）评分较高，评估者间无显著差异，53.3%的评价中更偏好AI摘要。结果表明，隐私保护的本地部署小型语言模型能可靠生成具有临床意义的纵向摘要，优于部分医疗专家，可为临床决策提供支持。
原文摘要 · Abstract (English)
Chronic dermatologic diseases such as pemphigus require long-term follow-up, generating extensive longitudinal clinical documentation that is difficult to review comprehensively during routine visits and increasing clinician workload as well as the risk of missing critical historical information. We evaluated whether a locally deployed, privacy-preserving small language model (SLM) could retrieve structured clinical features and generate longitudinal summaries from long-term dermatology follow-up records. In this retrospective case series, thirty pemphigus patients contributed 541 visit notes that were aggregated into full longitudinal records (89,336 words); 56 clinically relevant features were annotated by two expert dermatologists. The locally deployed SLM (Qwen3 4B Thinking 2507) was queried with each complete record to retrieve 56 features and generate one final report summaries. Across 1,680 feature retrieval tasks, mean accuracy was 82.25%. Dermatologists' ratings of AI-generated summaries were high for overall quality (8.23-8.47), clinical accuracy (7.93-8.20), and usefulness (8.47-8.50), with no significant inter-evaluator differences and an overall preference for AI summaries in 53.3% of evaluations. These findings suggest that privacy-preserving, locally deployed SLMs can outperform medical experts and reliably generate clinically meaningful longitudinal summaries. SLMs may support clinical decision-making when integrated with appropriate oversight.
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