Bias in Medical AI Models

Most medical AI models are trained on limited and non-diverse datasets (often Western-centric or from specific institutions), leading to biased predictions.

By using federated learning, it allows institutions globally to train models locally on their data—without ever sharing raw patient data—creating truly representative global models.

Data Privacy & Compliance (HIPAA, GDPR, etc.)

Patient data is highly sensitive, and regulations make it difficult to centralize datasets.

With federated learning and encrypted weight sharing, it ensures no sensitive data leaves its origin, making compliance much easier while still enabling collaborative learning.

Access to AI for Underserved Medical Facilities

Smaller hospitals or clinics often don't have access to cutting-edge AI tools for diagnostics or analytics.

By offering a Model Hive, these institutions can access pretrained diagnostic models (hosted on HuggingFace), improving patient care in resource-constrained settings.

Fragmentation in AI Development in Healthcare

Many institutions work in silos, leading to duplicated efforts and non-shareable insights.

It builds a centralized ecosystem (Model Hive) where models, learnings, and updates can be transparently accessed and improved—fostering collaboration and innovation.

Lack of Interpretability and Monitoring in Medical AI

Clinicians and regulators demand visibility into how AI models perform and evolve.

Uses MLFlow integration for performance tracking and Admin Portal for transparency, version control, and decision-making around model deployment.

Patient Outcomes & Early Diagnosis

Delayed or inaccurate diagnoses for conditions like breast cancer, glaucoma, or pneumonia can be fatal.

Delayed or inaccurate diagnoses for conditions like breast cancer, glaucoma, or pneumonia can be fatal.

Core Purpose

Privacy-first AI model training & sharing

Unified health ID + data portability

AI diagnostics + NHS AI standards

Privacy-focused FL research

Model development from static datasets

Privacy & Security

✅ Federated learning, no raw data shared

⚠️ Data centralized, protected under policy

⚠️ Centralized but with NHS governance

✅ Strong privacy via FL

❌ High risk of data leakage

Data Contribution

✅ From hospitals + individual contributors

❌ Only hospital/patient-linked records

❌ NHS-only systems

✅ Open to FL researchers

❌ Requires full data sharing

AI Diagnostic Models

✅ Public model hub (e.g., ECG, Pneumonia)

❌ Not AI-diagnostic focused

⚠️ Research-stage models

⚠️ Research-oriented demos

✅ But often biased or inaccessible

Compute Participation

✅ Contributor model (anyone with resources)

❌ No distributed compute support

❌ Central compute only

✅ FL testbeds

❌ Monolithic infrastructure

User Role Diversity

✅ Admin, Data Provider, Contributor, User

❌ Primarily patient + hospital roles

❌ Internal system users

✅ Flexible roles, but dev-focused

❌ No role management

Real-Time Model Monitoring

✅ MLFlow-based model tracking

❌ No AI performance metrics exposed

⚠️ Possibly internal dashboards

⚠️ Some visualizations

❌ Black-box systems

AI Assistant/Support

✅ Groq LLM chatbot + FAISS RAG

❌ Static dashboards & support

❌ Limited LLM use

⚠️ Technical, not UX-focused

❌ No LLM guidance

Target Impact Area

🌍 Diagnostic equity via global AI

🇮🇳 Health ID, records portability

🇬🇧 NHS AI integration

🌍 FL research testing

🌍 Model building for select areas

Openness/Extensibility

✅ Open source + API-driven architecture

❌ Mostly government-locked ecosystem

❌ Closed-loop NHS systems

✅ Open but niche

❌ Hard to adapt/extensible

Frontend Technologies

TypeScript, Next.js, Tailwind CSS

Backend Technologies

Python, FastAPI

Databases

Supabase

LLM Model APIs

Groq

Frontend Hosting

Vercel

Model Hive - Models

Huggingfaces Spaces

Federated Learning

Flower

Aggregated Learning

MLFlow

Containerization

Docker

Version Control

GitHub