Accuracy Audit: Built a Digital Atlas of Pathology — then used AI to audit itself.

I recently built an educational web app featuring 68 synthetic CNS histology slides across 15 disease categories. Each slide includes ~200-word microscopic descriptions following WHO 2021 classification standards.

But generating medical images with AI is one thing. Making sure they're accurate is another.

The audit pipeline:

• Ran a text accuracy audit against WHO 2021 CNS classification — found and fixed 8 nomenclature errors across 64 slides • Then used Google Gemini (gemini-2.5-flash) to analyze all 68 synthetic images for histological accuracy — rating each as Accurate, Partially Accurate, or Inaccurate

The results were humbling:

• 53 images rated INACCURATE • 15 rated PARTIALLY ACCURATE • 0 rated ACCURATE

That's a 0% accuracy rate on AI-generated medical images.

Used Gemini's specific feedback to generate medically precise regeneration prompts for all 68 slides and regenerated every single one.

The tech stack: React + TypeScript frontend, Express backend, Google Gemini API integration for batch image analysis with rate-limited parallel processing, all deployed on Replit.

Every image is clearly labeled as synthetic. Transparency matters in medical education.

The full audit trail — text corrections, image ratings, and regeneration actions — is documented and publicly accessible within the app.

AI can create. AI can also critique. The key is building the pipeline to do both.