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<p>Acute ischemic stroke (AIS) requires rapid integration of multimodal imaging, contraindication screening, and treatment selection within minutes, yet real-world decision-making is limited by fragmented workflows and uneven specialist availability. We developed an imaging-grounded multi-agent multimodal large language model (MLLM) framework that emulates a multidisciplinary stroke team for structured treatment recommendation. The framework decomposes the AIS workflow into coordinated agents for triage, time-window assessment, multimodal imaging interpretation, contraindication screening, and final treatment recommendation, with retrieval-augmented generation (RAG) incorporating evidence-grounded knowledge. We retrospectively evaluated the framework in 1,658 consecutive suspected-AIS cases from two centers. Compared with a single-prompt baseline, treatment recommendation accuracy improved from 0.636 to 0.755 with multi-agent coordination and further to 0.785 after RAG augmentation, while Macro-F1 increased from 0.562 to 0.732 and 0.768, respectively. The framework showed improvements in reperfusion pathways, more effectively retaining candidates for intravenous thrombolysis and mechanical thrombectomy. Imaging agent yielded sensitivity/specificity of 0.490/0.999 for non-contrast CT intracranial hemorrhage detection, 0.883/0.851 for CT angiography large-vessel occlusion identification, and 0.939/0.739 for CT perfusion deficit assessment. These results suggest that structured imaging-grounded multi-agent coordination enhances reliability and clinical alignment of MLLM-based decision support in AIS, supporting potential deployment in time-critical stroke care settings with limited specialist availability.</p>

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A Multi-Agent MLLM Framework for Imaging-Grounded Treatment Recommendation in Acute Ischemic Stroke

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Bicong Yan,
  • Zhuo Li,
  • Yanfeng Fan,
  • Ying Li,
  • Li Chen,
  • Xinyu Song,
  • Yixiao Tang,
  • Li Shen,
  • Ruipeng Zhang,
  • Yuehua Li

摘要

Acute ischemic stroke (AIS) requires rapid integration of multimodal imaging, contraindication screening, and treatment selection within minutes, yet real-world decision-making is limited by fragmented workflows and uneven specialist availability. We developed an imaging-grounded multi-agent multimodal large language model (MLLM) framework that emulates a multidisciplinary stroke team for structured treatment recommendation. The framework decomposes the AIS workflow into coordinated agents for triage, time-window assessment, multimodal imaging interpretation, contraindication screening, and final treatment recommendation, with retrieval-augmented generation (RAG) incorporating evidence-grounded knowledge. We retrospectively evaluated the framework in 1,658 consecutive suspected-AIS cases from two centers. Compared with a single-prompt baseline, treatment recommendation accuracy improved from 0.636 to 0.755 with multi-agent coordination and further to 0.785 after RAG augmentation, while Macro-F1 increased from 0.562 to 0.732 and 0.768, respectively. The framework showed improvements in reperfusion pathways, more effectively retaining candidates for intravenous thrombolysis and mechanical thrombectomy. Imaging agent yielded sensitivity/specificity of 0.490/0.999 for non-contrast CT intracranial hemorrhage detection, 0.883/0.851 for CT angiography large-vessel occlusion identification, and 0.939/0.739 for CT perfusion deficit assessment. These results suggest that structured imaging-grounded multi-agent coordination enhances reliability and clinical alignment of MLLM-based decision support in AIS, supporting potential deployment in time-critical stroke care settings with limited specialist availability.