MedOpenClaw: Auditable Medical Imaging Agents Reasoning over Uncurated Full Studies
This addresses the problem of developing clinically realistic medical imaging agents for researchers and practitioners, though it appears incremental by extending existing agentic frameworks to medical domains.
The paper tackles the oversimplification of medical imaging evaluation by introducing MEDOPENCLAW, an auditable runtime for vision-language models to navigate full 3D medical studies, and MEDFLOWBENCH, a benchmark covering brain MRI and lung CT/PET. Initial results show that while state-of-the-art models can solve basic tasks, their performance degrades with professional tools due to spatial grounding issues.
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.