BioInsight：多智能體協同編排實現互動式生物醫學知識發現

摘要

生物醫學研究人員越來越多地使用AI生成的分析和報告來解讀蛋白質層級的訊號，但靜態輸出往往不足以支持研究決策——使用者需要檢視證據、評估不確定性、比較機制並調整假說。我們提出BioInsight，這是一個多智能體系統，將靜態的生物醫學報告生成轉變為以證據為中心的互動式介面生成。給定疾病名稱、蛋白質關聯表以及可選的隊列元數據，BioInsight透過類型化的中間產物組織疾病特異性證據，包括排序後的路徑、文獻證據包、蛋白質層級推理筆記、引用支持的報告、儀表板模式以及渲染後的互動式介面。該系統將證據檢索與機制推理分離，通過確定性組件標準化引用引用，並將報告中使用的相同結構化證據轉換為互動式介面。我們在標準化生物醫學問答、具挑戰性的蛋白質功能推理以及端到端生物醫學證據綜述上評估BioInsight。結果顯示，BioInsight達到最佳表現，並表明生物醫學AI系統應超越純文字與靜態報告，轉向保留來源且互動式的證據產物。

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.