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Abstract - DEEPMED Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification
Navigating the deluge of heterogeneous medical data, from academic literature (PubMed) to clinical guidelines (Web) and private knowledge bases, remains a critical bottleneck for evidence-based medicine. While commercial black-box tools lack transparency, standard open-source RAG implementations frequently suffer from reasoning drift when handling complex, long-tail queries. We present DEEPMED Search, a fully open-source, agentic platform designed for transparent medical deep research. Built on a high-performance this http URL architecture, DEEPMED Search features a source-adaptive router that autonomously dispatches sub-queries to PubMed, web search, or local graph-based knowledge bases based on information density. Crucially, the platform integrates an introspective verification module, powered by a causal-consistent multi-agent debate framework, to validate retrieved evidence against diagnostic logic before synthesis. To demonstrate its robustness, we showcase DEEPMED Search's ability to autonomously decompose high-difficulty rare disease queries, filter out confounding noise, and generate structured, citation-backed research reports in minutes. By open-sourcing this software, we provide the community with a robust infrastructure to democratize access to trustworthy, glass-box medical reasoning in research and prototyping settings.
深度医学搜索:一个带有内省验证功能的开源智能医学深度研究平台 /
DEEPMED Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification
1️⃣ 一句话总结
该论文提出了一个名为DEEPMED Search的开源智能平台,能够自动从医学文献、临床指南和本地知识库中检索信息,并通过多智能体内部辩论机制验证证据的可靠性,从而高效生成带引用的结构化研究报告,解决了传统检索系统处理复杂罕见病问题时容易逻辑混乱的痛点。