SanjeevAI-A beacon of hope for Thalassemia patients

Inspiration

India has the world’s largest population of Thalassemia major patients, yet thousands still face delayed transfusions due to poor blood availability, lack of coordination, and limited awareness. Even when blood exists in a nearby district, patients often don't know. This reality inspired us to build SanjeevAI — an AI-powered, inclusive healthcare assistant that ensures no patient suffers or dies because a single unit of blood wasn’t available in time. Inspired by IndiaAI’s mission of People, Progress, Planet, our goal is to bring predictive, accessible, multilingual support to the most underserved families.

What it does

SanjeevAI is a unified AI platform that uses intelligence, automation, and geospatial mapping to ensure timely transfusions for Thalassemia patients.

Core Features

AI-based blood matching that identifies compatible donors instantly

Live map dashboard of donors, hospitals, blood banks, and donation drives

Predictive alerts for upcoming shortages and region-wise demand

Awareness & genetic counseling module in local languages

Privacy-first data infrastructure with anonymization

UPI-linked donor rewards to increase donor retention

SanjeevAI brings donors, hospitals, NGOs, and families onto one ecosystem — a “Google Maps for Life”.

How I built it

I designed SanjeevAI as a modular, scalable ecosystem:

Tech Stack

AI/ML: Demand forecasting, donor matching, anomaly detection

Cloud: Firebase/AWS for real-time data sync

Geospatial: Map APIs + clustering for “Blood Availability Heatmaps”

Backend: FastAPI / Node.js API layer

Frontend: React Native + Offline-first support

Security: Encryption, anonymization, role-based access

Architecture Workflow

Patient uploads transfusion schedule/history

AI model predicts next required transfusion window

Matching engine locates nearest compatible donors

Live map displays blood stock, drives, and emergency availability

Alerts sent to donors, families, and nearest hospitals

Challenges I ran into

Handling highly sensitive medical data while maintaining privacy

Designing an accurate blood-matching model with limited public datasets

Integrating real-time geospatial data and making it reliable in rural areas

Building an app that works even with low connectivity

Ensuring local-language accessibility without compromising usability

Accomplishments that I am proud of

Developed a complete end-to-end AI system architecture

Designed a predictive blood shortage model concept for emergency planning

Created a multilingual, inclusive UX suitable for rural and low-literacy users

Built a solution that aligns perfectly with IndiaAI’s mission and the Inclusion theme

Impact-focused design that can reduce transfusion delays by 40–60%

What I learned

How crucial real-time data is in healthcare systems

The complexities of matching donors to patients dynamically

Importance of cultural and linguistic inclusivity in AI

How AI can transform public health when applied responsibly

Challenges of bridging rural digital gaps with offline-first tech

What’s next for SanjeevAI – A Beacon of Hope for Thalassemia Patients The roadmap ahead:

Build a working MVP with real-time donor–patient matching

Partner with NGOs for field validation

Integrate with district-level blood banks and hospital APIs

Add voice-based chatbot in 8+ Indian languages

🇮🇳 Collaborate with National Health Stack & ABDM for scaling

Expand the model to support Hemophilia & CKD patients

Grow into a national platform for predictive, inclusive healthcare

Built With

• fastapi
• firebase
• machine-learning
• map
• native
• offlinefirstsupport
• react