用AI智能体+可视化，让科研人员高效探索海量医学文献
MedViz: An Agent-based, Visual-guided Research Assistant for Navigating Biomedical Literature
- 集成多个AI智能体与交互式可视化，支持文献探索
- 基于百万级论文语义地图，动态生成研究假设
- 适合需要快速梳理领域脉络的医学研究人员
生物医学研究者面临从数百万篇跨领域文献中导航的挑战。传统搜索引擎通常返回按相关性排序的文本列表，难以支持全局探索或深入分析。尽管生成式AI和大语言模型在摘要生成、信息抽取和问答任务中展现出潜力，但其基于对话的实现方式与文献检索工作流整合不佳。为此，我们提出MedViz，一个结合多智能体与交互式可视化的视觉分析系统，用于支持大规模生物医学文献的探索。MedViz将数百万篇论文的语义地图与代理驱动的查询、摘要和假设生成功能相结合，使研究者能够迭代优化问题、识别趋势并发现隐藏关联。通过将智能体与可视化融合，MedViz将文献搜索转变为动态探索过程，加速知识发现。
原文摘要 · Abstract (English)
Biomedical researchers face increasing challenges in navigating millions of publications in diverse domains. Traditional search engines typically return articles as ranked text lists, offering little support for global exploration or in-depth analysis. Although recent advances in generative AI and large language models have shown promise in tasks such as summarization, extraction, and question answering, their dialog-based implementations are poorly integrated with literature search workflows. To address this gap, we introduce MedViz, a visual analytics system that integrates multiple AI agents with interactive visualization to support the exploration of the large-scale biomedical literature. MedViz combines a semantic map of millions of articles with agent-driven functions for querying, summarizing, and hypothesis generation, allowing researchers to iteratively refine questions, identify trends, and uncover hidden connections. By bridging intelligent agents with interactive visualization, MedViz transforms biomedical literature search into a dynamic, exploratory process that accelerates knowledge discovery.
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