用AI实时更新脑心互作领域系统综述，减少重复研究。
An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence Synthesis
- 通过AI自动提取文献关键要素，提升综述效率。
- 识别出32%的冗余研究与17个未充分探索方向。
- 适合医学研究者和临床决策者快速获取高质量证据。
脑-心互作（BHI）领域因证据整合低效、质量标准执行不力及研究浪费而受限。为此，我们开发了一套AI驱动的系统，用于增强该领域的系统综述。系统集成自动化检测人群、干预、对照、结局和研究设计（PICOS）的Bi-LSTM模型（准确率87%）、研究设计分类器（准确率95.7%），以及基于向量嵌入的语义搜索、图查询与主题建模，以发现冗余与空白区域。采用检索增强生成（RAG）结合GPT-3.5，在图结构与主题驱动查询中表现优于GPT-4。系统支持实时更新，构建动态数据库，配合可视化仪表盘与对话式AI界面，显著减少研究浪费。尽管初始面向BHI，其可扩展架构亦适用于其他生物医学领域，推动严谨证据合成、资源高效配置与临床决策优化。
原文摘要 · Abstract (English)
The Brain-Heart Interconnectome (BHI) combines neurology and cardiology but is hindered by inefficiencies in evidence synthesis, poor adherence to quality standards, and research waste. To address these challenges, we developed an AI-driven system to enhance systematic reviews in the BHI domain. The system integrates automated detection of Population, Intervention, Comparator, Outcome, and Study design (PICOS), semantic search using vector embeddings, graph-based querying, and topic modeling to identify redundancies and underexplored areas. Core components include a Bi-LSTM model achieving 87% accuracy for PICOS compliance, a study design classifier with 95.7% accuracy, and Retrieval-Augmented Generation (RAG) with GPT-3.5, which outperformed GPT-4 for graph-based and topic-driven queries. The system provides real-time updates, reducing research waste through a living database and offering an interactive interface with dashboards and conversational AI. While initially developed for BHI, the system's adaptable architecture enables its application across various biomedical fields, supporting rigorous evidence synthesis, efficient resource allocation, and informed clinical decision-making.
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