masa-med-ai

masa-med-ai / pubmed-systematic-review

Public

Pubmed MCPと連携して簡易的なSRを行うSkill

19
2
69% credibility
Found May 30, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This project is a systematic review tool for medical research. It helps researchers automatically search PubMed (a database of medical and scientific papers), filter relevant articles based on criteria they define, and organize findings for creating comprehensive literature reviews. The tool uses AI to assist with the time-consuming task of sorting through thousands of papers to find the most relevant ones for a given research question.

How It Works

1
🔬 You need to review medical research

You discover this tool when you need to systematically review scientific papers from PubMed for your research or study.

2
📋 You gather your research questions

You define what medical questions you want answered and what criteria papers must meet to be included in your review.

3
🤖 Your AI assistant searches and filters

The system automatically searches PubMed for relevant papers and uses AI to filter and organize the results based on your criteria.

4
📄 You review the curated results

You receive a organized list of papers that match your research questions, ready for your detailed analysis.

5
📊 You extract key findings

You pull out important data and conclusions from the selected papers to build your systematic review.

✅ Your systematic review is complete

You have a thorough, well-organized summary of the medical evidence that answers your original research questions.

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AI-Generated Review

What is pubmed-systematic-review?

A tool that integrates with PubMed MCP (Model Context Protocol) to help researchers perform simplified systematic reviews. It automates literature searching, filtering, and data extraction from PubMed's database using AI. Researchers define search parameters, and the system retrieves and organizes relevant clinical papers, reducing weeks of manual work to hours.

Why is it gaining traction?

Systematic reviews are essential in evidence-based medicine but notoriously labor-intensive. This project appeals to researchers because it leverages modern AI workflows to handle the repetitive parts: querying PubMed's API, applying systematic review filters, and building a structured dataset. The MCP integration means it fits into AI-native toolchains, making it compatible with newer LLM applications. With the rise of RAG architectures and AI-assisted research, developers want solutions that bridge PubMed's vast clinical database with AI pipelines.

Who should use this?

Medical researchers conducting literature reviews for publications or meta-analyses. Clinical data scientists building knowledge bases for diagnostic AI. Academic developers creating research automation tools. Healthcare AI teams needing structured access to PubMed's clinical literature for training or augmentation purposes.

Verdict

The concept is solid and addresses a real pain point, but the project shows early-stage signs: only 19 stars, a 0.7% credibility score, and a binary README that limits transparency. This is a proof-of-concept for researchers comfortable exploring experimental tools. Evaluate it as a foundation to build upon rather than a production-ready solution. Check documentation carefully before committing.

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