小模型正重塑医疗信息化，兼顾效率与隐私。
The Rise of Small Language Models in Healthcare: A Comprehensive Survey
- 构建医疗小模型分类框架，涵盖架构、优化与部署路径。
- 在临床任务中实现媲美大模型的性能，资源消耗更低。
- 适合关注医疗隐私、边缘计算与落地部署的研究者。
尽管大型语言模型在医疗应用中取得显著进展，但数据隐私担忧和资源限制日益突出。小型语言模型（SLMs）因其可扩展性与临床可行性，成为下一代医疗信息学中资源受限环境下的高效解决方案。本综述提出一个分类框架，帮助医疗从业者与信息学家识别和归类医疗SLMs。通过梳理医疗SLM的发展时间线，建立涵盖自然语言处理任务、利益相关者角色与护理连续性的三维分析框架。本文系统梳理了从零构建模型的架构基础，通过提示工程、指令微调与推理增强实现临床精度；并借助压缩技术提升模型的可访问性与可持续性。核心目标是为医疗专业人士提供全面综述，介绍模型优化最新进展，并提供精选资源以支持未来研究。我们汇总了广泛研究的医疗NLP任务中的实验结果，凸显SLMs的变革潜力。更新后的代码库已开源至GitHub。
原文摘要 · Abstract (English)
Despite substantial progress in healthcare applications driven by large language models (LLMs), growing concerns around data privacy, and limited resources; the small language models (SLMs) offer a scalable and clinically viable solution for efficient performance in resource-constrained environments for next-generation healthcare informatics. Our comprehensive survey presents a taxonomic framework to identify and categorize them for healthcare professionals and informaticians. The timeline of healthcare SLM contributions establishes a foundational framework for analyzing models across three dimensions: NLP tasks, stakeholder roles, and the continuum of care. We present a taxonomic framework to identify the architectural foundations for building models from scratch; adapting SLMs to clinical precision through prompting, instruction fine-tuning, and reasoning; and accessibility and sustainability through compression techniques. Our primary objective is to offer a comprehensive survey for healthcare professionals, introducing recent innovations in model optimization and equipping them with curated resources to support future research and development in the field. Aiming to showcase the groundbreaking advancements in SLMs for healthcare, we present a comprehensive compilation of experimental results across widely studied NLP tasks in healthcare to highlight the transformative potential of SLMs in healthcare. The updated repository is available at Github
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