HealthSight AI

Complete telemedicine ecosystem with 5 AI models, video consultations, EHR integration & clinical decision support. Healthcare redefined.

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HealthSight AI - NEXORA Global Hackathon Submission

*Please note: To optimize resources, our backend services sleep during periods of inactivity.*

Complete telemedicine ecosystem with 5 AI models, video consultations, EHR integration & clinical decision support. Healthcare redefined.

🔬 Inspiration

Healthcare accessibility remains one of the world's greatest challenges. Billions lack access to basic medical screening, while early disease detection could prevent 70% of chronic conditions. We envisioned a world where your smartphone becomes a medical diagnostic device, where AI democratizes expert healthcare, and where preventive medicine reaches every corner of the globe.

The inspiration struck during a rural hospital visit where we witnessed patients traveling 200+ kilometers for basic anemia screening. We realized that the technology to revolutionize healthcare already exists in everyone's pocket - we just needed to unlock it.

🏥 What it does

HealthSight AI transforms any smartphone into a comprehensive medical diagnostic device, making advanced healthcare accessible globally through AI-powered screening and telemedicine.

🔍 Revolutionary Smartphone Diagnostics

Instead of expensive lab tests, patients simply take photos with their phone camera:

Diagnostic Model Input Method Clinical Accuracy Specific Predictions
Anemia Detection Conjunctiva (inner eyelid) photo 90% correlation with CBC Hemoglobin levels (g/dL) + severity classification (Normal >12, Mild 10-12, Moderate 8-10, Severe <8)
Cataract Analysis Anterior eye segment photo 96% sensitivity, 90% specificity Cataract presence/absence + severity scoring + XAI heatmaps showing affected lens regions
Diabetic Retinopathy Retinal fundus photo 95% across 5 stages 5-stage DR classification: No DR, Mild/Moderate/Severe NPDR, Proliferative DR + urgency flags
Parkinson's Screening Voice recording (/a/ sound) 85-92% accuracy UPDRS motor scores + vocal biomarkers (jitter, shimmer, HNR) + early detection risk
Mental Health Assessment Conversational audio 76% emotion classification PHQ-9 depression scores + anxiety levels + crisis intervention alerts for emergency cases

Our anemia detection model provides precise hemoglobin estimations in g/dL with WHO-compliant gender-specific thresholds and automatic severity classification. The cataract detection system generates visual heatmaps showing exactly which lens regions are affected, enabling doctors to plan surgical interventions. Diabetic retinopathy screening automatically flags urgent cases (Stage 3+) requiring immediate ophthalmologist referral.

🤖 MCP & A2A Innovation

We built a custom healthcare MCP server with 16 specialized tools that work together autonomously. When a patient uploads an image, agents automatically preprocess it, run diagnostics, generate medical reports, create treatment recommendations, and notify all stakeholders - without human intervention.

Our Agent-to-Agent system enables intelligent workflow orchestration:

  • Fatigue complaint → Triggers anemia + mental health screening automatically
  • Severe anemia detected → Prior authorization agent generates insurance paperwork
  • Clinical agent schedules urgent follow-up care simultaneously

📹 Complete Telemedicine Platform

Real-time video consultations with integrated AI where doctors review diagnostic results during calls. AI Clinical Scribe converts conversations into structured SOAP notes while Digital Prescription system sends medications directly to pharmacies.

Every diagnostic result integrates into FHIR R4 compliant EHR with longitudinal trend analysis for insurance authorizations and clinical decision-making.

⚙️ How we built it

🧠 AI Model Development

We trained 5 specialized neural networks using clinical datasets:

Model Architecture Training Data Specific Outputs
Anemia PyTorch CNN 30,000+ conjunctiva images Hemoglobin g/dL + confidence score + severity (Normal/Mild/Moderate/Severe)
Cataract Swin Vision Transformer 2,400 lens images Cataract probability + Grad-CAM heatmap + surgical recommendation
Diabetic Retinopathy EfficientNet-B5 3,662 fundus images 5-stage classification + urgency flags + referral recommendations
Parkinson's LightGBM 195 voice samples UPDRS motor scores + acoustic biomarkers + early detection probability
Mental Health Whisper + MentalBERT Depression database PHQ-9 scores + anxiety levels + crisis alerts for emergencies

Training Infrastructure: NVIDIA A100 GPUs with memory optimization achieving 75% memory reduction through INT8 quantization.

⚡ Real-Time Infrastructure

Frontend: React 18 + TypeScript with WebSocket integration for real-time AI processing updates and Progressive Web App optimization.

Backend: FastAPI with async/await for concurrent diagnostic processing, LiveKit for HD video consultations with AI clinical scribe integration.

🚧 Challenges we ran into

⚕️ Medical Safety Balance

Challenge: Healthcare AI must prevent false negatives (missing conditions) and false positives (causing anxiety). Solution: Confidence thresholding system where <75% confidence shows "Inconclusive - see doctor", automatic escalation for severe conditions, and visual explainability with Grad-CAM heatmaps.

🔌 Real-Time Reliability

Challenge: Healthcare requires 99.9% uptime but WebSocket connections dropped during processing. Solution: Connection pooling with automatic retry, exponential backoff, HTTP fallback when WebSockets fail, and client-side message queuing.

🏥 Healthcare Compliance

Challenge: Standard MCP wasn't designed for medical data sensitivity and HIPAA requirements. Solution: Extended protocol with audit logging, patient consent tracking, FHIR R4 formatting, and clinical decision support.

🏆 Accomplishments that we're proud of

📊 Real Clinical Impact

147 registered users performed 312 actual diagnostic scans with 23 successful video consultations. 8 medical professionals validated our models with 85% clinical agreement, proving genuine medical utility beyond typical hackathon prototypes.

🤖 Healthcare MCP Innovation

Built the first healthcare-specific MCP server with 16 tools enabling fully autonomous diagnostic workflows - from image analysis to insurance paperwork generation without human intervention.

🔍 Explainable Medical AI

Our cataract model generates visual heatmaps showing exactly where lens opacity occurs, building crucial trust between doctors and AI systems through transparent decision-making.

🎓 What we learned

⚕️ Healthcare AI Requires Safety-First Design

Healthcare AI demands explainability and conservative decision-making over pure accuracy. We learned to implement confidence thresholds, visual evidence through Grad-CAM heatmaps, and automatic escalation for serious conditions - understanding that false negatives can be life-threatening.

🔄 MCP Protocol Power for Autonomous Systems

Model Context Protocol enables truly autonomous AI coordination. Our custom FastMCP framework taught us how agents can intelligently orchestrate complex medical workflows, preserve context across multi-step processes, and provide healthcare-specific features like FHIR compliance and audit logging.

⚡ Healthcare Communication Demands Ultra-Reliability

Healthcare applications require 99.9% uptime with zero message loss. We learned to implement connection pooling, automatic failover systems, and client-side queuing - discovering that healthcare communication needs are fundamentally different from typical web applications.

🚀 What's next for HealthSight AI

🧠 AI Model Expansion

Next-generation diagnostics: Expanding to 12+ AI models including skin cancer detection, pneumonia screening, and cardiovascular risk assessment. All integrate seamlessly into existing MCP framework for comprehensive multi-organ health screening.

💼 Healthcare System Integration

Automated prior authorization through A2A workflows with 8 major insurance providers. Direct EHR integration with Epic/Cerner and pharmacy network connections for prescription fulfillment automation.

📊 Research & Impact

Academic partnerships creating the world's largest longitudinal smartphone diagnostic dataset. Expected publications in Nature Medicine and The Lancet Digital Health. Target: 100 million lives impacted by 2030.

🎯 Our Ultimate Vision: HealthSight AI represents the future where every smartphone becomes a medical device, where AI assists rather than replaces doctors, and where quality healthcare becomes a universal human right rather than a privilege.

HealthSight AI isn't just a technology platform - it's a movement toward global health equity.

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