{"type":"ScholarlyArticle","title":"DeepMed Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification","authors":[{"name":"Maolin Liu","slug":"maolin-liu","given":"Maolin","family":"Liu","affiliation":""},{"name":"Fanyu Xu","slug":"fanyu-xu","given":"Fanyu","family":"Xu","affiliation":""},{"name":"Ruoqing Xu","slug":"ruoqing-xu","given":"Ruoqing","family":"Xu","affiliation":""},{"name":"JiaHang Zhang","slug":"jiahang-zhang","given":"Jiahang","family":"Zhang","affiliation":""},{"name":"Hao Wang","slug":"hao-wang","given":"Hao","family":"Wang","affiliation":""},{"name":"Rui Wang","slug":"rui-wang","given":"Rui","family":"Wang","affiliation":""}],"abstract":"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 Next.js 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 at a commercial-grade performance level, which is publicly available at: https://www.deepmedsearch.cloud and the demonstration video is available at: https://youtu.be/4U4aok8yLpk.","doi":"10.24963/ijcai.2026/978","url":"https://ijcai.org/proceedings/2026/978","pdf":"https://ijcai.org/proceedings/2026/0978.pdf","markdown":"https://ijcai.org/proceedings/2026/978.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/978","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Demo Track","track":"Demo Track","number":978,"pages":{"first":8453,"last":8457},"keywords":["AI: AI Ethics, Trust, Fairnes","AI: Agent-based and Multi-agent Systems"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}