SenseAI — AI-Powered Rural Healthcare Assistant

Bringing intelligent, real-time clinical guidance to community health workers — even without internet.

Inspiration

Access to timely healthcare guidance remains a major challenge in rural and low-resource areas. Community Health Workers (CHWs) often have to make critical decisions with limited tools, delayed diagnostics, and poor connectivity.

We were inspired to build a solution that could assist them with real-time risk assessment and actionable guidance — even without internet access.

What It Does

SenseAI is an AI-powered rural healthcare assistant that helps assess patient conditions and provide structured medical guidance. It supports multiple conditions including:

Maternal Hemorrhage
Tuberculosis Adherence
Pesticide Exposure
Diabetic Foot Ulcers (DFU)

The system collects patient inputs, processes them through specialized AI agents, and outputs:

Output	Description
📊 Risk Score & Level	Quantified severity assessment
📝 Clear Explanation	Human-readable reasoning
✅ Actionable Checklist	Step-by-step guidance for CHWs
⚠️ Missing Data Indicators	Flags incomplete assessments

It works across multiple platforms:

📱 Mobile App
💬 WhatsApp Chatbot
📞 Voice Call Agent

How We Built It

SenseAI uses a hybrid AI architecture designed for real-world, low-resource environments.

Frontend

Flutter mobile application for Community Health Workers

Backend

Flask APIs handling different diagnosis agents

AI Models

Model	Role
DeepSeek	Reasoning and structured decision-making
VisionPro + Custom Segmentation	DFU image analysis
Gemma 2B (quantized)	Offline inference via `llama.cpp`

Integrations

💬 WhatsApp Cloud API — Chat-based interaction
🎙️ ElevenLabs — Voice-based AI agent

Each condition is handled by a dedicated agent with structured scoring logic to ensure reliable and consistent outputs.

Challenges We Ran Into

Running AI models offline on low-end devices due to RAM and storage limitations
Managing real-time switching between online and offline modes
Ensuring AI outputs were not random by enforcing structured scoring logic
Integrating multiple platforms (Flutter, WhatsApp, Voice) into a single workflow
Handling image-based diagnosis reliably with limited datasets

🏆 Accomplishments We're Proud Of

✅ Built a fully functional hybrid AI system (online + offline fallback)
✅ Developed multi-channel access (mobile app, WhatsApp, voice calls)
✅ Implemented clinical-style scoring instead of generic AI responses
✅ Successfully integrated image-based DFU analysis
✅ Created a scalable solution tailored for rural healthcare challenges

📚 What We Learned

"Prompt engineering alone is not enough — structured reasoning is critical."

Offline AI deployment requires strong optimization and trade-offs
Simplicity in UI/UX is essential for real-world adoption
Multi-platform systems need strong coordination and data consistency
Building for real-world constraints is very different from building demos

What's Next for SenseAI

[ ] Optimize offline models to run on low-end devices
[ ] Expand support to more diseases — malaria, anemia, child health
[ ] Add real-time doctor consultation and telemedicine integration
[ ] Improve image analysis with better datasets and model training
[ ] Support more regional languages and voice interactions
[ ] Reduce dependency on paid APIs using open-source alternatives

Tech Stack

Frontend       → Flutter (Mobile App)
Backend        → Flask (Python)
AI (Online)    → DeepSeek, VisionPro
AI (Offline)   → Gemma 2B via llama.cpp
Messaging      → WhatsApp Cloud API
Voice          → ElevenLabs
Image Analysis → Custom Segmentation Model

Built with ❤️ for the communities that need it most.