agentic-evidence-seeking-clinical

name	agentic-evidence-seeking-clinical
category	deep-learning
description	ClinSeekAgent methodology for automated multimodal evidence seeking in clinical reasoning - shifting from passive evidence consumption to active evidence acquisition across heterogeneous medical data sources
trigger	ClinSeekAgent, clinical evidence seeking, agentic clinical reasoning, multimodal evidence, automated evidence acquisition, clinical agent, EHR navigation, medical tool invocation, evidence-seeking agent

Agentic Evidence Seeking for Clinical Reasoning

Methodology for building clinical AI agents that actively seek, iteratively plan, and synthesize multimodal evidence from heterogeneous sources, rather than consuming pre-curated evidence.

Core Problem

Existing clinical LLM systems assume evidence has already been curated and handed to the model. Real-world clinical workflows require agents to actively gather evidence from raw, heterogeneous sources.

ClinSeekAgent Framework

Three-Phase Evidence Seeking Cycle

Phase 1: Evidence Gathering

• Query medical knowledge bases (drug databases, guidelines, literature)
• Navigate raw EHRs (electronic health records) to extract relevant patient data
• Invoke medical imaging tools (CXR analysis, lab result interpretation)
• Access only raw data sources — no pre-curated evidence provided

Phase 2: Hypothesis Refinement

• Refine clinical hypotheses as new information emerges
• Dynamically adjust evidence-seeking strategy based on findings
• Iterative planning: decide what evidence to seek next

Phase 3: Evidence Integration

• Synthesize collected multimodal evidence
• Generate grounded clinical decisions
• Produce auditable reasoning traces

Dual-Use Architecture

Inference-Time Agent

• Runs with frontier LLMs at inference time
• Dynamic evidence seeking for individual clinical queries
• +15.1 F1 improvement on multimodal tasks (Claude Opus 4.6: 47.5 → 62.6)

Training-Time Pipeline

• Distills high-quality agent trajectories into compact models
• ClinSeek-35B-A3B achieves +11.9 F1 over baseline on AgentEHR-Bench
• Approaches frontier LLM performance with 35B parameter model

Implementation Pattern

class ClinSeekAgent:
    def __init__(self, llm, data_sources):
        self.llm = llm
        self.data_sources = data_sources  # EHR, knowledge bases, imaging tools
        self.hypotheses = []
        self.evidence = []
    
    def seek_evidence(self, clinical_query):
        """Phase 1: Actively gather evidence from raw sources."""
        plan = self.llm.generate_evidence_plan(clinical_query)
        for source in plan.sources:
            evidence = self.data_sources[source].query(plan.queries[source])
            self.evidence.append(evidence)
    
    def refine_hypotheses(self):
        """Phase 2: Update hypotheses based on new evidence."""
        self.hypotheses = self.llm.update_hypotheses(
            self.hypotheses, self.evidence
        )
        # Decide if more evidence is needed
        if self.need_more_evidence():
            self.seek_evidence(next_queries)
    
    def integrate_and_decide(self):
        """Phase 3: Synthesize evidence into clinical decision."""
        return self.llm.clinical_decision(self.evidence, self.hypotheses)

Benchmark: ClinSeek-Bench

• Pairs Curated Input reasoning with Automated Evidence-Seeking
• Evaluates both text-only EHR tasks and multimodal tasks
• Shows consistent improvement across 9 host models on risk prediction

Performance

• Text-only: Claude Opus 4.6 from 60.0 → 63.2 F1; MiniMax M2.5 from 43.1 → 47.3
• Multimodal: Claude Opus 4.6 from 47.5 → 62.6 (+15.1)
• All evaluated models improve across CXR-related task groups
• Distilled model: +11.9 F1 over Qwen3.5-35B-A3B baseline

When to Use

• Clinical decision support with raw, uncurated data sources
• Multimodal clinical reasoning (text + imaging + lab results)
• Building compact clinical models via trajectory distillation
• Any domain requiring active evidence acquisition from heterogeneous sources

Activation

clinical evidence seeking, agentic clinical reasoning, multimodal evidence acquisition, ClinSeekAgent, EHR navigation agent, medical tool invocation, evidence-seeking pipeline, clinical trajectory distillation

Reference

arXiv: 2605.20176v1 - "ClinSeekAgent: Automating Multimodal Evidence Seeking for Agentic Clinical Reasoning"