Offline AI medical assistant for rural Africa - Africa Deep Tech Challenge 2026
# ⚕️ MediAssist — Offline Medical Assistant
### Africa Deep Tech Challenge 2026 | Healthcare & Medical Domain
> AI-powered medical triage and guidance that runs 100% offline on an 8GB laptop.
> No cloud. No GPU. No internet required.
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## Problem
Rural communities across Nigeria and Africa face a critical gap: qualified health workers are scarce, and when patients need medical guidance, the nearest clinic may be hours away. Cloud-based AI health tools require stable internet — a luxury unavailable in most rural settings.
**MediAssist solves this by bringing intelligent medical assistance directly to the device.**
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## Solution
A lightweight offline medical chatbot built on **Qwen2.5 3B** running via **Ollama**, served through a **Python Flask** backend with a clean HTML/JS frontend. It supports:
- Symptom triage and referral decisions
- Medication guidance and dosing
- Maternal and child health
- General primary care Q&A
- 5 languages: English, Nigerian Pidgin, Hausa, Yoruba, Igbo
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## ADTC Compliance
| Requirement | Status |
|-------------|--------|
| Runs 100% offline | ✅ |
| No cloud dependency | ✅ |
| No discrete GPU | ✅ |
| Fits in 8GB RAM | ✅ (~2.5GB RAM usage) |
| Ubuntu 22.04 compatible | ✅ |
| Healthcare domain | ✅ |
**Estimated scores:**
- Sacc: High — medically safe, structured, multilingual responses
- Sperf: ~15–20 TPS on i5 10th–13th gen (num_thread=4, num_ctx=2048)
- Seff: ~65/100 — Peak RAM ~2.5GB out of 7GB budget
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## Tech Stack
| Component | Choice | Why |
|-----------|--------|-----|
| LLM | Qwen2.5 3B | Best multilingual quality + lowest RAM on 8GB laptop |
| Runtime | Ollama | Simple local model server, cross-platform |
| Backend | Python Flask | Lightweight, no overhead |
| Frontend | Vanilla HTML/CSS/JS | Zero dependencies, works in any browser |
| RAM usage | ~2.5GB | Leaves headroom for OS and app |
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## Quick Start
### 1. Install Ollama
```bash
# Ubuntu
curl -fsSL
ollama.com | sh
# Windows: …