用密集检索技术自动筛选医学综述相关文献，大幅节省人工筛查时间。
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense Retrieval
- 基于密集检索与用户反馈，动态优先排序待筛文献。
- 相比传统方法，提升筛选效率与准确率，支持实时交互。
- 提供网页工具与开源库，适合研究者快速搭建新筛选流程。
医学系统性综述的文献筛选耗时且费力，常需处理数万篇研究。通过优先筛选相关文献，可提前启动后续综述任务并节省时间。此前工作已开发一种密集检索方法，在标题与摘要筛选阶段结合审阅者反馈，显著优于以往主动学习方法。本演示进一步扩展该工作：(1) 构建基于网页的筛选工具，支持用户利用前沿技术高效筛选文献；(2) 提供集成模型与反馈机制的 Python 库，便于研究者开发和验证新型主动学习方法。文中介绍工具设计，并展示其在实际筛选中的应用。工具地址：https://densereviewer.ielab.io，源代码开源：https://github.com/ielab/densereviewer。
原文摘要 · Abstract (English)
Screening is a time-consuming and labour-intensive yet required task for medical systematic reviews, as tens of thousands of studies often need to be screened. Prioritising relevant studies to be screened allows downstream systematic review creation tasks to start earlier and save time. In previous work, we developed a dense retrieval method to prioritise relevant studies with reviewer feedback during the title and abstract screening stage. Our method outperforms previous active learning methods in both effectiveness and efficiency. In this demo, we extend this prior work by creating (1) a web-based screening tool that enables end-users to screen studies exploiting state-of-the-art methods and (2) a Python library that integrates models and feedback mechanisms and allows researchers to develop and demonstrate new active learning methods. We describe the tool's design and showcase how it can aid screening. The tool is available at https://densereviewer.ielab.io. The source code is also open sourced at https://github.com/ielab/densereviewer.
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