MedOpenClaw: Auditable Medical Imaging Agents Reasoning over Uncurated Full Studies
Currently, evaluating vision-language models (VLMs) in medical imaging tasks oversimplifies clinical reality by relying on pre-selected 2D images that demand significant manual labor to curate. This setup misses the core challenge of realworld diagnostics: a true clinical agent must actively navigate full 3D volumes across multiple sequences or modalities to gather evidence and ultimately support a final decision. To address this, we propose MEDOPENCLAW, an auditable runtime designed to let VLMs operate dynamically within standard medical tools or viewers (e.g., 3D Slicer). On top of this runtime, we introduce MEDFLOWBENCH, a full-study medical imaging benchmark covering multi-sequence brain MRI and lung CT/PET. It systematically evaluates medical agentic capabilities across viewer-only, tool-use, and open-method tracks. Initial results reveal a critical insight: while state-of-the-art LLMs/VLMs (e.g., Gemini 3.1 Pro and GPT-5.4) can successfully navigate the viewer to solve basic study-level tasks, their performance paradoxically degrades when given access to professional support tools due to a lack of precise spatial grounding. By bridging the gap between static-image perception and interactive clinical workflows, MEDOPENCLAW and MEDFLOWBENCH establish a reproducible foundation for developing auditable, full-study medical imaging agents.
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Weixiang Shen
claimed_verified
2026-03-31T09:29:39.569Z
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Shen
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Jakob3033
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Yanzhu Hu
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Che Liu
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Junde Wu
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Jiayuan Zhu
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Chengzhi Shen
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Min Xu
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Yueming Jin
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Benedikt Wiestler
69ca364e4c8e8981b7fa7b8c
Daniel Rueckert
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Jiazhen Pan
claimed_verified
2026-03-31T09:29:42.039Z
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JZPeterPan
JZPeterPan
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JZPeterPan
MedOpenClaw: Auditable Medical Imaging Agents Reasoning over Uncurated Full Studies
Currently, evaluating vision-language models (VLMs) in medical imaging tasks oversimplifies clinical reality by relying on pre-selected 2D images that demand significant manual labor to curate. This setup misses the core challenge of realworld diagnostics: a true clinical agent must actively navigate full 3D volumes across multiple sequences or modalities to gather evidence and ultimately support a final decision. To address this, we propose MEDOPENCLAW, an auditable runtime designed to let VLMs operate dynamically within standard medical tools or viewers (e.g., 3D Slicer). On top of this runtime, we introduce MEDFLOWBENCH, a full-study medical imaging benchmark covering multi-sequence brain MRI and lung CT/PET. It systematically evaluates medical agentic capabilities across viewer-only, tool-use, and open-method tracks. Initial results reveal a critical insight: while state-of-the-art LLMs/VLMs (e.g., Gemini 3.1 Pro and GPT-5.4) can successfully navigate the viewer to solve basic study-level tasks, their performance paradoxically degrades when given access to professional support tools due to a lack of precise spatial grounding. By bridging the gap between static-image perception and interactive clinical workflows, MEDOPENCLAW and MEDFLOWBENCH establish a reproducible foundation for developing auditable, full-study medical imaging agents.
MEDOPENCLAW and MEDFLOWBENCH enable evaluation of vision-language models in medical imaging by allowing dynamic interaction with 3D medical volumes through standard viewers, revealing limitations in spatial reasoning when accessing professional tools.
vision-language models medical imaging 3D volumes medical agents spatial grounding interactive clinical workflows medical imaging benchmark medical tool integration