ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning
Researchers have developed ClinSeekAgent, a novel framework designed to enhance clinical reasoning in large language models by enabling them to actively seek and synthesize multimodal evidence. Unlike previous approaches that rely on pre-selected data, ClinSeekAgent dynamically queries medical knowledge bases, navigates electronic health records, and utilizes imaging tools to gather information. This active evidence-seeking process significantly improves the performance of models like Claude Opus 4.6 and MiniMax M2.5 on both text-only and multimodal clinical tasks, as demonstrated by the creation of the ClinSeek-Bench benchmark. AI
IMPACT Enhances LLM capabilities in clinical settings by enabling active evidence acquisition, potentially improving diagnostic accuracy and decision support.
