MedFlowBench evaluates VLM agents on full radiology and pathology studies by requiring both task answers and verifiable evidence like key slices and regions of interest, revealing that answer-only scores overestimate performance.
Guttag, and Adrian V
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VERITAS is a multi-agent system for verifiable hypothesis testing on multimodal clinical MRI datasets that achieves 81.4% verdict accuracy with frontier models and introduces an epistemic evidence labeling framework.
GAZE framework with viewer tools and literature retrieval achieves 58.2 mAP@0.3 lesion localization and 34.9% top-1 diagnostic accuracy on 906 rare brain MRI cases in zero-shot setting, with larger gains on rarest pathologies.
Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.
citing papers explorer
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MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows
MedFlowBench evaluates VLM agents on full radiology and pathology studies by requiring both task answers and verifiable evidence like key slices and regions of interest, revealing that answer-only scores overestimate performance.
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VERITAS: Verifiable Epistemic Reasoning for Image-Derived Hypothesis Testing via Agentic Systems
VERITAS is a multi-agent system for verifiable hypothesis testing on multimodal clinical MRI datasets that achieves 81.4% verdict accuracy with frontier models and introduces an epistemic evidence labeling framework.
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GAZE: Grounded Agentic Zero-shot Evaluation with Viewer-Level Tools and Literature Retrieval on Rare Brain MRI
GAZE framework with viewer tools and literature retrieval achieves 58.2 mAP@0.3 lesion localization and 34.9% top-1 diagnostic accuracy on 906 rare brain MRI cases in zero-shot setting, with larger gains on rarest pathologies.
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AgentRx: A Benchmark Study of LLM Agents for Multimodal Clinical Prediction Tasks
Single-agent LLM frameworks outperform naive multi-agent systems in multimodal clinical risk prediction tasks and are better calibrated.