用大模型加速医学文献综述，支持直接筛选标题摘要。
AiReview: An Open Platform for Accelerating Systematic Reviews with LLMs
- 构建可扩展的LLM框架，专用于文献标题与摘要筛选
- 提供网页界面，实现透明可追踪的AI辅助筛选流程
- 首个面向医学综述的LLM辅助平台，代码开源可复用
系统性综述是循证医学的基础，但其创建过程耗时费力，主要因需筛选大量研究以决定纳入。现有工具多依赖传统机器学习方法，而大语言模型（LLMs）展现出进一步加速筛选的潜力。然而，当前尚无工具允许用户直接使用LLMs进行筛选，也缺乏对LLM辅助筛选流程的系统化与透明化支持。本文提出：(i) 一个可扩展的框架，用于将LLMs应用于系统性综述任务，特别是标题和摘要筛选；(ii) 一个基于网页的LLM辅助筛选界面。二者结合形成AiReview——首个连接前沿LLM筛选技术与医学系统性综述创建的平台。该工具已在 https://aireview.ielab.io 开放使用，源码同步开源于 https://github.com/ielab/ai-review。
原文摘要 · Abstract (English)
Systematic reviews are fundamental to evidence-based medicine. Creating one is time-consuming and labour-intensive, mainly due to the need to screen, or assess, many studies for inclusion in the review. Several tools have been developed to streamline this process, mostly relying on traditional machine learning methods. Large language models (LLMs) have shown potential in further accelerating the screening process. However, no tool currently allows end users to directly leverage LLMs for screening or facilitates systematic and transparent usage of LLM-assisted screening methods. This paper introduces (i) an extensible framework for applying LLMs to systematic review tasks, particularly title and abstract screening, and (ii) a web-based interface for LLM-assisted screening. Together, these elements form AiReview-a novel platform for LLM-assisted systematic review creation. AiReview is the first of its kind to bridge the gap between cutting-edge LLM-assisted screening methods and those that create medical systematic reviews. The tool is available at https://aireview.ielab.io. The source code is also open sourced at https://github.com/ielab/ai-review.
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