HelpRag — AI Diagnostic Bias Detection Tool

A full-stack medical AI tool that analyzes diagnostic images and flags potential demographic bias in AI confidence scores, cross-referenced against peer-reviewed research baselines.

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What It Does

Medical AI models have documented accuracy disparities across demographic groups — particularly across Fitzpatrick skin types. HelpRag makes this visible by:

• Running AI image analysis via Groq (Llama 4 Scout vision model)
• Cross-referencing AI confidence against published bias baselines (Daneshjou et al., Nature Medicine 2024)
• Flagging scans where confidence falls below the expected threshold for that demographic
• Tracking scan history with search, filter, and PDF export

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Tech Stack

Layer	Technology
Frontend	Next.js 15, React 19, Tailwind CSS, shadcn/ui
Backend	FastAPI, Python 3.13
AI Model	Groq API — meta-llama/llama-4-scout-17b-16e-instruct
Charts	Recharts
State	localStorage + sessionStorage

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Features

• New Scan — Upload medical image + patient demographics (age, gender, Fitzpatrick scale, body localization)
• Scan Types — Chest X-ray, Skin Lesion, Dermoscopy, Mammography, CT Scan, MRI
• Bias Evaluation — Confidence vs baseline comparison with risk level (low / moderate / high)
• Dashboard — Live stats and confidence-by-Fitzpatrick chart from scan history
• History — Search, filter by bias/scan type/risk, sort, delete, export to PDF
• Human Review — One-time flag to request clinical verification, tracked in history
• PDF Export — Full diagnostic report with bias evaluation

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Project Structure

Helprag/
├── Backend/
│   ├── main.py           # FastAPI app + /scan endpoint
│   ├── requirements.txt  # Python dependencies
│   └── .env              # GROQ_API_KEY (not committed)
└── Frontend/
    ├── app/
    │   ├── layout.tsx
    │   └── (app)/
    │       ├── page.tsx          # Dashboard
    │       ├── scan/page.tsx     # New scan form
    │       ├── results/page.tsx  # Diagnosis + bias results
    │       ├── history/page.tsx  # Scan history
    │       └── methodology/page.tsx
    ├── components/
    ├── lib/
    │   ├── api.ts          # API types + fetch helper
    │   └── pdf-export.ts   # PDF report generator
    └── .env.local          # API URL (not committed)

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Setup & Running

Prerequisites

• Python 3.10+
• Node.js 18+
• Groq API key — free at console.groq.com

Backend

cd Backend
pip install -r requirements.txt

Create a .env file in the Backend/ folder:

GROQ_API_KEY=your_groq_api_key_here

Run the backend:

# Mac/Linux
uvicorn main:app --reload --port 8000

# Windows PowerShell
uvicorn main:app --reload --port 8000

Frontend

cd Frontend
npm install

Create a .env.local file in the Frontend/ folder:

NEXT_PUBLIC_API_URL=http://localhost:8000

Run the frontend:

npm run dev

Open http://localhost:3000

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Bias Baselines Reference

Confidence thresholds are derived from:

• Daneshjou et al. — Disparities in dermatology AI performance on a diverse, curated clinical image set. Nature Medicine, 2024
• Seyyed-Kalantari et al. — Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nature Medicine, 2021

Fitzpatrick Type	Skin Description	Dermoscopy Baseline	Skin Lesion Baseline
Type I	Pale white	92%	94%
Type II	White	91%	93%
Type III	Light brown	88%	90%
Type IV	Moderate brown	83%	86%
Type V	Dark brown	74% ⚠	78% ⚠
Type VI	Deeply pigmented	67% ⚠	71% ⚠

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API Endpoints

Method	Endpoint	Description
POST	/scan	Analyze image + return diagnosis and bias flag
GET	/health	Health check
GET	/baselines	Full bias baselines JSON

POST /scan — Form Fields

Field	Type	Required	Description
image	file	Yes	Medical image (JPEG/PNG, max 50MB)
scan_type	string	Yes	e.g. skin-lesion, dermoscopy, chest-xray
fitzpatrick	string	Yes	1 through 6
age	string	No	Patient age
gender	string	No	Patient gender
localization	string	No	Body location (skin-lesion/dermoscopy only)

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Disclaimer

This tool is a research prototype for demonstrating AI diagnostic bias. It is not a substitute for professional medical diagnosis. All results should be reviewed by a qualified clinician.