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Medicine

ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning

Juncheng Wu, Letian Zhang

Featured May 28, 2026

Original Paper

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Simply

Instead of just reading pre-made patient summaries, a new AI system called ClinSeekAgent acts like a smart detective, actively searching through patient records, looking up medical facts online, and analyzing X-rays to make better clinical decisions.

In depth
The paper introduces ClinSeekAgent, an automated agentic framework that enables large language models (LLMs) to actively seek and synthesize multimodal evidence for clinical decision support. Instead of relying on pre-curated data, the framework allows LLMs to dynamically query electronic health records (EHRs), perform web searches for medical knowledge, and utilize medical imaging tools. This dynamic evidence acquisition significantly improves performance on complex clinical tasks, especially those requiring sparse, longitudinal, or multimodal information, and can also be used as a training pipeline to distill these capabilities into smaller open-source models.

Key Takeaways

  • 1
    Introduces ClinSeekAgent, an agentic framework for dynamic multimodal evidence seeking in clinical reasoning, moving beyond passive evidence consumption.
  • 2
    Demonstrates significant performance improvements for frontier LLMs on text-only and multimodal clinical tasks by enabling active tool use for EHR retrieval, web search, and medical imaging analysis.
  • 3
    Validates ClinSeekAgent as a training pipeline for open-source models, successfully distilling complex evidence-seeking behaviors into smaller agents and achieving state-of-the-art performance.

Conceptual Flow

HIGH LEVEL
1
Methodology: Active Evidence Seeking

Instead of just getting pre-sorted information, the AI actively looks for clues from different places, like patient files, the internet, and medical pictures, to solve a medical puzzle.

Patient Question
Ask AI Agent
Search Patient Records
Search Web
Analyze Images
2
Results: Improved Clinical Decisions

By actively searching for clues, the AI makes much better predictions for patient risks and conditions, especially when the information is hidden in many different places.

Old Way: Fixed Info
New Way: AI Searches
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Lower Accuracy
Higher Accuracy