AI-Powered Clinical Diagnostic Assistant
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.
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.
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.
Fuses structured EHR data, medical imaging (X-ray, ECG), and free-text symptom narratives.
SHAP-powered explanations show clinicians exactly which factors drove each differential.
Full diagnostic capability without internet — syncs when connectivity is restored.
Native integration with existing EHR systems via FHIR R4 standard APIs.
Clinic and district-level dashboards tracking disease prevalence and diagnostic trends.
Federated learning allows the model to improve from clinic data without centralising PHI.
Field visits to 12 clinics, clinician interviews, regulatory mapping, dataset sourcing.
Multi-modal architecture, training on 2M+ annotated cases, SHAP explainability integration.
FastAPI services, HL7 FHIR R4 adapter, HIPAA-compliant data pipeline, audit logging.
React Native offline-first app, on-device inference for common conditions, sync engine.
10 pilot clinics, clinician training, real-world accuracy benchmarking, feedback loops.
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.
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