LLM4Sweat：一个用于多汗症支持的可信大语言模型
LLM4Sweat: A Trustworthy Large Language Model for Hyperhidrosis Support
AI总结：
本文提出LLM4Sweat，一个面向多汗症的开源领域专用大语言模型框架，通过合成数据增强、微调和专家迭代评估，实现可信且富有同理心的诊断、治疗建议与心理支持，并优于基线模型。
摘要：
尽管大语言模型（LLMs）在医疗健康领域展现出潜力，但它们在罕见疾病中的应用仍受到微调所需数据集稀缺且不可靠的制约。多汗症是一种导致出汗超过生理需求的疾病，正是这样一种罕见病，影响2-3%的人群，并显著影响身体舒适度和心理社会福祉。迄今为止，尚无工作针对多汗症的诊断或护理对大语言模型进行定制化改造。为填补这一空白，我们提出了LLM4Sweat，一个开源且领域专用的大语言模型框架，用于提供可信且富有同理心的多汗症支持。该系统遵循三阶段流水线。在数据增强阶段，一个前沿大语言模型基于精心整理的开源数据生成医学上合理的合成病例片段，以创建多样且平衡的问答数据集。在微调阶段，一个开源基础模型在该数据集上进行微调，以提供诊断、个性化治疗建议和富有同理心的心理支持。在推理与专家评估阶段，临床和心理学专家评估准确性、适当性和同理心，经过验证的回复会迭代地丰富数据集。实验表明，LLM4Sweat优于基线模型，并交付了首个面向多汗症的开源大语言模型框架，为具有类似数据和可信度挑战的其他罕见疾病提供了一种可推广的方法。
英文摘要：
While large language models (LLMs) have shown promise in healthcare, their application for rare medical conditions is still hindered by scarce and unreliable datasets for fine-tuning. Hyperhidrosis, a disorder causing excessive sweating beyond physiological needs, is one such rare disorder, affecting 2-3% of the population and significantly impacting both physical comfort and psychosocial well-being. To date, no work has tailored LLMs to advance the diagnosis or care of hyperhidrosis. To address this gap, we present LLM4Sweat, an open-source and domain-specific LLM framework for trustworthy and empathetic hyperhidrosis support. The system follows a three-stage pipeline. In the data augmentation stage, a frontier LLM generates medically plausible synthetic vignettes from curated open-source data to create a diverse and balanced question-answer dataset. In the fine-tuning stage, an open-source foundation model is fine-tuned on the dataset to provide diagnosis, personalized treatment recommendations, and empathetic psychological support. In the inference and expert evaluation stage, clinical and psychological specialists assess accuracy, appropriateness, and empathy, with validated responses iteratively enriching the dataset. Experiments show that LLM4Sweat outperforms baselines and delivers the first open-source LLM framework for hyperhidrosis, offering a generalizable approach for other rare diseases with similar data and trustworthiness challenges.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
