Medical Research Agent (PubMed Evidence Synthesizer)
An autonomous clinical research assistant that queries PubMed, extracts randomized control trial evidence, compares conflicting studies, and synthesizes summaries with citations.
The Problem
What difficult challenge is this experiment tackling?Over 1 million biomedical papers are published annually, making it nearly impossible for busy clinicians and researchers to review conflicting clinical evidence manually.
The Hypothesis
What do I believe autonomous AI & engineering can accomplish?A multi-agent RAG workflow dedicated to PubMed Central can extract study methodologies, sample sizes, and p-values to deliver structured meta-analysis overviews.
System Architecture & Workflow
End-to-end design, data pipelines, and component interactionBenchmark & Empirical Metrics
Accuracy • Latency • Cost • Token Usage • Human Intervention100% DOI citation verification; extraction of sample size, hazard ratio, and p-values verified across 50 peer-reviewed papers.
Security, Privacy & Ethical Guardrails
Risk mitigation, strict boundaries, and sandbox controlsOpen-access academic literature only; full compliance with NCBI Entrez API query limits.
Lessons Learned & Observations
Empirical findings from prototyping and testingMeSH (Medical Subject Headings) term translation drastically improves paper retrieval relevance.
Next Experiment Iteration
Upcoming modifications, scaling factors, or new hypothesesAdd visual forest plot generation for clinical meta-analyses.