DeepMed Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification
Maolin Liu, Fanyu Xu, xu ruoqing, JiaHang Zhang, Hao Wang, Rui Wang
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.
BibTeX
@inproceedings{ijcai2026_deepmedsearchano,
title = {DeepMed Search: An Open-Source Agentic Platform for Medical Deep Research with Introspective Verification},
author = {Maolin Liu and Fanyu Xu and xu ruoqing and JiaHang Zhang and Hao Wang and Rui Wang},
booktitle = {IJCAI 2026},
year = {2026}
}