🛡️ AegisLab AI

A clinical diagnostic copilot that analyzes patient medical biomarkers in real-time to uncover hidden health risks.

AegisLab AI is an advanced clinical laboratory intelligence system designed to assist lab technicians and physicians. By performing multi-parameter clinical reasoning on laboratory test results, it identifies hidden relationships between test markers, detects early disease signatures, and provides explainable, actionable diagnostic insights.

✨ Key Features

• 🤖 Multi-Parameter AI Reasoning: Goes beyond standard reference ranges by analyzing the complex relationships between different biomarkers (e.g., cross-referencing Hemoglobin, MCV, and Ferritin).
• 🚨 Critical Risk Detection: Automatically categorizes patient risk levels (LOW, MODERATE, HIGH, CRITICAL) and instantly flags life-threatening abnormalities like severe electrolyte imbalances or acute organ failure.
• 📈 Longitudinal Trend Analysis: Tracks and visualizes patient history over time using interactive charts, allowing clinicians to monitor disease progression or treatment efficacy at a glance.
• 🧠 Explainable Diagnostic Reports: Generates professional, human-readable clinical summaries detailing the "why" behind the AI's diagnostic confidence scores.
• 🔐 Secure & Frictionless Access: Protected by Firebase Google Sign-In, ensuring only authorized medical personnel can access sensitive patient data.
• ⚡ Resilient Architecture: Powered primarily by the Google Gemini API, with an automatic OpenAI fallback mechanism to guarantee uninterrupted operation.

🛠️ Tech Stack

• Frontend: HTML5, CSS3, Vanilla JavaScript, Chart.js
• Backend: Python, FastAPI, SQLAlchemy (Async)
• Database: TiDB (MySQL-compatible distributed SQL)
• AI Engine: Google Gemini (Primary) & OpenAI (Fallback)
• Authentication: Firebase Auth & Firebase Admin SDK

👥 Contributors Collistus Kibe - Project Lead & Core AI/Backend Engineer

Dennis Gerrard- Frontend and mostly JS expert

Wokabi Staney - Backend Dev

Built With

• css
• html
• javascript
• python