Medical deep research agent with reasoning chain analysis
MedResearcher-R1 is a medical deep research agent that combines clinical reasoning chains with iterative literature search to answer complex medical questions. Unlike general research agents, it is specialized for medical evidence — understanding clinical trial designs, PICO frameworks, evidence hierarchies, and medical terminology. Uses reasoning chain analysis (R1) to decompose clinical questions and systematically gather evidence.
Clinical Question
↓
R1 Reasoning Chain (decompose into sub-questions)
↓
Medical Search Agent
├── PubMed (MeSH terms)
├── ClinicalTrials.gov
├── Cochrane Library
└── WHO ICTRP
↓
Evidence Extraction Agent
├── PICO extraction
├── Study design classification
├── Outcome extraction
└── Risk of bias assessment
↓
Synthesis Agent (evidence grading)
↓
Clinical Answer + Evidence Report
from med_researcher_r1 import MedResearcherR1
researcher = MedResearcherR1(
llm_provider="anthropic",
search_backends=["pubmed", "clinical_trials", "cochrane"],
)
# Complex clinical question
result = researcher.research(
question="In patients with treatment-resistant depression, "
"how does psilocybin-assisted therapy compare to "
"esketamine in terms of remission rates and "
"long-term outcomes?",
evidence_level="systematic", # systematic, rapid, scoping
max_papers=50,
)
print(result.summary)
print(f"\nEvidence quality: {result.evidence_grade}")
print(f"Papers analyzed: {len(result.papers)}")
# Inspect the R1 reasoning chain
for step in result.reasoning_chain:
print(f"\nStep {step.number}: {step.type}")
print(f" Question: {step.question}")
print(f" Strategy: {step.search_strategy}")
print(f" Findings: {step.key_finding}")
print(f" Next: {step.next_action}")
# Example chain:
# Step 1: DECOMPOSE — Split into psilocybin efficacy,
# esketamine efficacy, head-to-head comparisons
# Step 2: SEARCH — PubMed: psilocybin depression RCT
# Step 3: EXTRACT — 3 RCTs found, extract PICO + outcomes
# Step 4: SEARCH — PubMed: esketamine depression outcomes
# Step 5: SYNTHESIZE — Compare evidence, note no direct
# head-to-head trials exist
# Step 6: CONCLUDE — Indirect comparison with caveats
# GRADE methodology for evidence quality
for paper in result.papers[:5]:
print(f"\n{paper.title} ({paper.year})")
print(f" Design: {paper.study_design}")
print(f" Sample: {paper.sample_size}")
print(f" Grade: {paper.evidence_grade}")
print(f" Risk of bias: {paper.risk_of_bias}")
# Aggregate evidence
print(f"\nOverall certainty: {result.certainty}")
# HIGH / MODERATE / LOW / VERY LOW
print(f"Recommendation: {result.recommendation}")
researcher = MedResearcherR1(
search_config={
"pubmed": {
"use_mesh": True,
"date_range": "2019/01/01:2025/12/31",
"article_types": [
"Randomized Controlled Trial",
"Meta-Analysis",
"Systematic Review",
],
},
"clinical_trials": {
"status": ["Completed", "Active, not recruiting"],
"phase": ["Phase 3", "Phase 4"],
},
},
reasoning_config={
"max_chain_length": 10,
"reflection_enabled": True,
"uncertainty_explicit": True,
},
)