EviSearch自动化提取临床试验数据并确保每条记录可追溯，支持医生审核。
EviSearch: A Human in the Loop System for Extracting and Auditing Clinical Evidence for Systematic Reviews
- 多智能体系统从原始PDF直接提取文本、表格、图表数据
- 在肿瘤学论文测试中准确率显著优于传统解析方法
- 生成可审计的溯源信息，适合临床研究者参与验证
我们提出EviSearch，一种多智能体提取系统，可直接从原始临床试验PDF中自动化构建与本体对齐的证据表，并保证每个单元格的溯源信息以供审计和人工验证。该系统结合保留排版布局和图表的PDF查询代理、检索引导的搜索代理以及页面级验证模块，在智能体意见不一致时强制进行页面级核对。整个流程针对多模态证据源（文本、表格、图表）实现高精度提取，并生成可供审稿人操作的溯源信息，供临床医生检查与修正。在由临床医生标注的肿瘤学试验论文基准上，EviSearch相比强基线解析模型显著提升提取准确性，同时实现全面的溯源覆盖。通过记录校对决策和审稿人修改，系统生成结构化的偏好与监督信号，推动模型迭代优化。EviSearch旨在加速动态系统综述流程，减轻手动标注负担，并为将大模型提取技术安全引入证据综合流程提供可审计路径。
原文摘要 · Abstract (English)
We present EviSearch, a multi-agent extraction system that automates the creation of ontology-aligned clinical evidence tables directly from native trial PDFs while guaranteeing per-cell provenance for audit and human verification. EviSearch pairs a PDF-query agent (which preserves rendered layout and figures) with a retrieval-guided search agent and a reconciliation module that forces page-level verification when agents disagree. The pipeline is designed for high-precision extraction across multimodal evidence sources (text, tables, figures) and for generating reviewer-actionable provenance that clinicians can inspect and correct. On a clinician-curated benchmark of oncology trial papers, EviSearch substantially improves extraction accuracy relative to strong parsed-text baselines while providing comprehensive attribution coverage. By logging reconciler decisions and reviewer edits, the system produces structured preference and supervision signals that bootstrap iterative model improvement. EviSearch is intended to accelerate living systematic review workflows, reduce manual curation burden, and provide a safe, auditable path for integrating LLM-based extraction into evidence synthesis pipelines.
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