BioInsight：面向交互式生物医学知识发现的多智能体编排

摘要

生物医学研究人员越来越多地使用人工智能生成的分析和报告来解释蛋白质水平信号，但静态输出通常不足以支持研究决策，用户需要检查证据、评估不确定性、比较机制并完善假设。我们提出了BioInsight，这是一个多智能体系统，将静态的生物医学报告生成转变为以证据为中心的交互式界面生成。给定疾病名称、蛋白质关联表以及可选队列元数据，BioInsight通过类型化的中间产物组织疾病特异性证据，包括排序通路、文献证据包、蛋白质水平推理笔记、基于引用的报告、仪表盘模式以及渲染后的交互式界面。该系统将证据检索与机制推理分离，通过确定性组件对引用进行标准化，并将报告中使用的相同结构化证据转化为交互式界面。我们在标准生物医学问答、具有挑战性的蛋白质功能推理以及端到端生物医学证据合成任务上评估了BioInsight。结果表明，BioInsight取得了最佳性能，并提示生物医学AI系统应超越纯文本和静态报告，转向保留溯源并支持交互的证据产物。

English

Biomedical researchers increasingly use AI-generated analyses and reports to interpret protein-level signals, but static outputs are often insufficient for research decision-making, where users need to inspect evidence, assess uncertainty, compare mechanisms, and refine hypotheses. We present BioInsight, a multi-agent system that moves from static biomedical report generation to interactive evidence-centered interactive interface generation. Given a disease name, a protein association table, and optional cohort metadata, BioInsight organizes disease-specific evidence through typed intermediate artifacts, including ranked pathways, literature evidence packets, protein-level reasoning notes, citation-grounded reports, dashboard schemas, and rendered interactive interfaces. The system decomposes evidence retrieval from mechanistic reasoning, normalizes citations through deterministic components, and converts the same structured evidence used in the report into an interactive interface. We evaluate BioInsight on standardized biomedical QA, challenging protein-function reasoning, and end-to-end biomedical evidence synthesis. Results show that BioInsight achieves best, and suggest that biomedical AI systems should move beyond text-only and static reports toward provenance-preserving, interactive evidence artifacts.