Rebuilding a $1.5M Medical AI Agent Pipeline with LangGraph: Six Agents Explained
MaryamMiradi · x · 2026-10-04
Most medical AI dies in deployment: clinicians don't trust black-box models, and data scientists burn 80% of their time cleaning messy data while hospitals spend $1.5M a year on broken systems.
Maryam Miradi rebuilt a paper's pipeline in Python with LangGraph, built from six helper agents:
- File Reader: instantly reads CSV, Excel, JSON and ZIP files, auto-expands folders inside archives and routes each file to the right place;
- Privacy Guard: detects names, emails, patient IDs and social security numbers, masks them, and blacks out text in medical images for automatic privacy protection;
- The remaining agents (Data Reader, etc.) handle parsing and downstream steps.
The core idea: split medical data ingestion, de-identification and parsing across specialized agents so the pipeline is interpretable enough to earn clinical trust.
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