HealthTech

MedSync AI

AI-Powered Clinical Diagnostic Assistant

ClientMedSync Health Systems
Duration18 months
Team14 engineers + 3 medical advisors
Year2023
🏥

📋 Overview

MedSync wanted to bring AI-assisted diagnostics to resource-limited clinics where specialist access is scarce. We built a HIPAA-compliant diagnostic assistant that analyses patient vitals, lab results, and medical imaging to surface differential diagnoses ranked by probability, with full explainability for clinicians.

⚠️ The Challenge

Rural and semi-urban clinics had 1 GP for every 4,000 patients and zero specialist access. Misdiagnosis rates were running at 34% for complex presentations. Existing AI tools were black boxes that clinicians didn't trust, had no explainability, and couldn't operate on low-bandwidth connections.

💡 Our Solution

We developed a multi-modal AI pipeline combining structured EHR data with imaging analysis (chest X-ray, ECG, retinal scans) and NLP-parsed symptom narratives. Every prediction includes a SHAP-based explanation showing which factors drove the result. The system works offline-first with background sync.

Results That Speak

🎯34%Reduction in Misdiagnosis
🧠89%Diagnostic Accuracy (top-3)
🏥80+Clinics Deployed
⏱️8 minAvg. Diagnostic Time (was 45)
👥200K+Patients Screened
🔒HIPAAFully Certified

Key Features

🧠

Multi-Modal AI

Fuses structured EHR data, medical imaging (X-ray, ECG), and free-text symptom narratives.

💡

Explainable Predictions

SHAP-powered explanations show clinicians exactly which factors drove each differential.

📱

Offline-First Mobile

Full diagnostic capability without internet — syncs when connectivity is restored.

🔗

HL7 FHIR Integration

Native integration with existing EHR systems via FHIR R4 standard APIs.

📈

Population Analytics

Clinic and district-level dashboards tracking disease prevalence and diagnostic trends.

🎓

Continuous Learning

Federated learning allows the model to improve from clinic data without centralising PHI.

Technology Stack

AI / ML

PythonPyTorchMONAIHugging FaceSHAP

Backend

FastAPICeleryRedisPostgreSQLHL7 FHIR

Mobile

React NativeExpoSQLite (offline)Background Sync

Infrastructure

AWS GovCloudDockerKubernetesMLflow

Security

Field EncryptionAudit LogsHIPAA BAASOC 2

Project Timeline

01

Clinical Discovery

8 weeks

Field visits to 12 clinics, clinician interviews, regulatory mapping, dataset sourcing.

02

Model Development

16 weeks

Multi-modal architecture, training on 2M+ annotated cases, SHAP explainability integration.

03

Backend & FHIR Layer

12 weeks

FastAPI services, HL7 FHIR R4 adapter, HIPAA-compliant data pipeline, audit logging.

04

Mobile App

10 weeks

React Native offline-first app, on-device inference for common conditions, sync engine.

05

Pilot Deployment

8 weeks

10 pilot clinics, clinician training, real-world accuracy benchmarking, feedback loops.

06

Full Rollout

10 weeks

80+ clinic deployment, MLflow monitoring, federated learning activation, documentation.

The explainability was the game-changer. Clinicians didn't trust AI until they could see why it was suggesting a diagnosis. Miraya Tech Lab understood that nuance and built it in from day one. The accuracy numbers speak for themselves.
SM
Dr. Sarah Mitchell
Product Director, MedSync Health Systems
★★★★★

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