MedBrowseComp: Benchmarking Medical Deep Research and Computer Use

Shan Chen; Pedro Moreira; Yuxin Xiao; Sam Schmidgall; Jeremy Warner; Hugo Aerts; Thomas Hartvigsen; Jack Gallifant; Danielle S. Bitterman

arXiv · 2505.14963

MedBrowseComp: Benchmarking Medical Deep Research and Computer Use

Shan Chen·Pedro Moreira·Yuxin Xiao·Sam Schmidgall·Jeremy Warner·Hugo Aerts·Thomas Hartvigsen·Jack Gallifant·Danielle S. Bitterman

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Abstract

Large language models (LLMs) are increasingly envisioned as decision-support tools in clinical practice, yet safe clinical reasoning demands integrating heterogeneous knowledge bases -- trials, primary studies, regulatory documents, and cost data -- under strict accuracy constraints. Existing evaluations often rely on synthetic prompts, reduce the task to single-hop factoid queries, or conflate reasoning with open-ended generation, leaving their real-world utility unclear. To close this gap, we present MedBrowseComp, the first benchmark that systematically tests an agent's ability to reliably retrieve and synthesize multi-hop medical facts from live, domain-specific knowledge bases. MedBrowseComp contains more than 1,000 human-curated questions that mirror clinical scenarios where practitioners must reconcile fragmented or conflicting information to reach an up-to-date conclusion. Applying MedBrowseComp to frontier agentic systems reveals performance shortfalls as low as ten percent, exposing a critical gap between current LLM capabilities and the rigor demanded in clinical settings. MedBrowseComp therefore offers a clear testbed for reliable medical information seeking and sets concrete goals for future model and toolchain upgrades. You can visit our project page at: https://moreirap12.github.io/mbc-browse-app/

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Shan Chen, Pedro Moreira, Yuxin Xiao, Sam Schmidgall, Jeremy Warner, Hugo Aerts, Thomas Hartvigsen, Jack Gallifant, Danielle S. Bitterman. 2025-05-20. MedBrowseComp: Benchmarking Medical Deep Research and Computer Use. https://arxiv.org/abs/2505.14963

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