How Medow Works
Transforming Healthcare with a Privacy-Preserving and Adaptive AI Platform
Medow leverages federated learning to ensure patient data never leaves the hospital, while creating continuously improving AI diagnostic models personalized for each institution through doctor feedback. Our closed-loop system harnesses network effects to enhance accuracy and efficiency over time, addressing India's doctor shortage (1:1,456 ratio) and data waste (95% unused, ₹50,000 Cr annual loss).
1. Upload or Enter Patient Data
Clinicians securely upload medical scans, images, or input symptoms via EHR/PACS integration. All data stays on-site, ensuring full privacy compliance with DPDP Act (reduces re-identification risks to <1%).
2. AI-Powered Diagnostic Analysis
The locally hosted AI model analyzes input using asymmetric algorithms for imbalanced data, providing predictions with explainability (e.g., heatmaps). Projected: Up to 94.7% accuracy in rare event detection like sepsis.
3. Clinician Feedback Loop
Doctors validate or correct outputs, providing intuitive labeling that refines the model in real-time. This doctor-in-the-loop approach ensures AI evolves with human expertise, projecting 99.7% accuracy improvements over time.
4. Federated Model Update
Updates combine learnings across the network without sharing raw data, using edge computing for low-latency. Enables network effects: More hospitals contribute to collective intelligence, building Medow's first-mover moat.
5. Continuous Improvement & Deployment
Enhanced models deploy locally, improving diagnostics and features like triage/supply chain optimization. Projected: 60% workload reduction and 30% faster throughput, creating self-reinforcing value for all users.
Harnessing Network Effects
With each hospital contributing feedback, our AI models grow smarter and more accurate—creating a winner-take-most dynamic (e.g., 23% efficiency gains from collaborative learning [PMC 2025]).
[Animated infographic of hospitals connecting in a network]
Privacy By Design
Federated learning ensures sensitive data never leaves secure environments, adhering to DPDP and global standards. This future-proofs Medow while enabling monopoly-building data flywheels.