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.

翻译：生物医学研究人员在导航跨领域的数百万篇文献时面临日益严峻的挑战。传统搜索引擎通常以排序文本列表的形式返回文章，对全局探索或深入分析的支持有限。尽管生成式人工智能与大型语言模型的最新进展在摘要生成、信息抽取和问答等任务中展现出潜力，但其基于对话的实现方式与文献检索工作流程的整合度不足。为弥补这一缺口，我们提出了MedViz——一个融合多智能体与交互式可视化的视觉分析系统，用以支持大规模生物医学文献的探索。MedViz将数百万篇文献的语义图谱与基于智能体的查询、摘要生成及假设构建功能相结合，使研究人员能够迭代式地精炼问题、识别趋势并发现潜在关联。通过将智能体与交互式可视化相桥接，MedViz将生物医学文献检索转化为动态的探索过程，从而加速知识发现。