Multi-agent system for biomedical literature review and synthesis
Med-Researcher is a multi-agent system designed specifically for biomedical literature review. It orchestrates specialized agents for searching PubMed and other medical databases, extracting structured evidence from clinical papers, and synthesizing findings into evidence-graded summaries. Particularly useful for clinical evidence reviews, drug interaction research, and systematic reviews in medicine.
Query → Planning Agent (decomposes clinical question)
↓
Search Agent (PubMed, PMC, clinical trials)
↓
Extraction Agent (PICO, outcomes, evidence grade)
↓
Synthesis Agent (evidence summary, contradictions)
↓
Report Agent (structured review output)
| Agent | Role |
|---|
| Planner | Converts clinical question to PICO format, generates sub-queries |
| Searcher | Queries PubMed, PMC, ClinicalTrials.gov |
| Extractor | Extracts structured data: population, intervention, outcomes |
| Synthesizer | Grades evidence, identifies consensus and contradictions |
| Reporter | Generates formatted review with citations |
from med_researcher import MedResearcher
researcher = MedResearcher(
llm_provider="anthropic",
search_backends=["pubmed", "pmc", "clinical_trials"],
)
# Clinical question
result = researcher.review(
question="What is the comparative efficacy of SGLT2 inhibitors "
"versus GLP-1 receptor agonists for cardiovascular "
"outcomes in type 2 diabetes?",
max_papers=50,
evidence_grading=True,
)
print(result.summary)
print(f"Papers analyzed: {len(result.papers)}")
print(f"Evidence grade: {result.overall_grade}")
# Automatic PICO extraction from clinical question
pico = researcher.extract_pico(
"Does metformin reduce cancer incidence in diabetic patients?"
)
# P: patients with diabetes
# I: metformin treatment
# C: no metformin / other antidiabetics
# O: cancer incidence
# Search with PICO components
result = researcher.review_pico(
population="type 2 diabetes patients",
intervention="metformin",
comparison="placebo or other antidiabetics",
outcome="cancer incidence",
)
# Evidence levels following GRADE methodology
for paper in result.papers:
print(f"{paper.title}")
print(f" Study type: {paper.study_type}") # RCT, cohort, case-control
print(f" Evidence level: {paper.evidence_level}") # High/Moderate/Low/Very Low
print(f" Risk of bias: {paper.bias_risk}")
print(f" Sample size: {paper.sample_size}")
# Aggregate evidence summary
print(f"\nOverall certainty: {result.certainty}")
print(f"Recommendation strength: {result.recommendation}")
researcher = MedResearcher(
search_config={
"pubmed": {
"max_results": 100,
"date_range": ("2020-01-01", "2025-12-31"),
"article_types": ["Clinical Trial", "Meta-Analysis",
"Randomized Controlled Trial"],
},
"clinical_trials": {
"status": ["Completed", "Active"],
"phase": ["Phase 3", "Phase 4"],
},
},
extraction_config={
"fields": ["population", "intervention", "comparator",
"primary_outcome", "secondary_outcomes",
"adverse_events", "sample_size", "follow_up"],
},
)
# Structured evidence table
result.export_evidence_table("evidence_table.csv")
# PRISMA flow diagram data
prisma = result.prisma_flow()
print(f"Identified: {prisma['identified']}")
print(f"Screened: {prisma['screened']}")
print(f"Included: {prisma['included']}")
# Bibliography
result.export_bibtex("references.bib")
# Full report
result.export_report("review.md", format="markdown")