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Health Analytics | AI Diagnostics

Turning terabytes of clinical data into sub‑second, life‑saving insights—HIPAA‑secure, FDA‑ready

The global market for AI‑driven diagnostics is already $1.59 billion (2024) and growing 22.4 % CAGR (Grand View Research). Yet diagnostic errors still cost the U.S. system $100 billion+ a year and harm hundreds of thousands of patients (NCBI). Studies show AI can boost cancer‑detection rates 20 % when paired with clinicians (Health), and the FDA has now cleared 1,016 AI/ML medical devices (U.S. Food and Drug Administration), signaling mainstream momentum—if health systems can deploy safely and at scale.

Our promise: VarenyaZ fuses real‑time data platforms, explainable ML, and rigorous validation so your care teams diagnose faster, miss less, and stay fully compliant.

The AI‑Diagnostics Pressure Cooker

Challenge
Reality
Impact if Ignored
Data Silos
70 % of imaging & lab data never reach analytics pipelines (internal audits)
Missed patterns, slower diagnoses
Bias & Explainability
Black‑box AI risks unequal outcomes and lawsuits
Regulatory hold‑ups, clinician distrust
Validation Burden
FDA requires locked weights, traceability, CPSO processes
Multi‑year delays
Latency to Bedside
> 500 ms inference adds 6 min average ED throughput
Crowded bays, diversion
Cyber & Privacy
HIPAA penalties average $2.1 M per breach
Fines, reputational damage

VarenyaZ Value Playbook

Unlock the full power of your clinical data with six core capabilities.

Unified Imaging & Lab Lake

DICOMweb + FHIR stream to Snowflake in < 1 min

Explainable ML Pipelines

SHAP & Grad‑CAM overlays for radiologists, CAP/CLIA tracebacks

Continuous Validation

MLOps with FDA SaMD pre‑checklists, locked‑weight snapshots

Edge & Cloud Inference

ONNX + NVIDIA Triton run CT in < 200 ms p95

Bias & Drift Sentinel

Feature drift, demographic parity, automated retrain triggers

Compliance by Design

HITRUST, HIPAA, GDPR, SOC 2 Type II—OPA policies & audit API

Modular Solution Stack

Composable architecture from data ingestion to explainable AI inference.

Data IngestLayer

Capability

DICOM/PACS, HL7, FHIR, device streams

Core Tech

Orthanc, DICOMweb, Debezium, Kafka

Feature StoreLayer

Capability

Real‑time + batch, lineage, TTL

Core Tech

Feast v3, Snowflake, Redis Vector

Model TrainingLayer

Capability

Vision, NLP, multimodal

Core Tech

PyTorch 2, MONAI, Hugging Face Transformers

Inference ServingLayer

Capability

GPU batch, CPU edge, auto‑scale

Core Tech

NVIDIA Triton, ONNX Runtime, KServe

XAI LayerLayer

Capability

SHAP, Grad‑CAM, Lime, What‑If

Core Tech

Captum, IBM AIX360

Clinical IntegrationLayer

Capability

SMART on FHIR apps, PACS overlays

Core Tech

React + MUI, Epic Hyperdrive

Observability & QALayer

Capability

Drift, latency, ROC, recall

Core Tech

Evidently AI, Grafana Cloud

GovernanceLayer

Capability

FDA SaMD trace, PHI masking

Core Tech

MLflow Registry, Vault, OPA Rego

Segment‑Specific Expertise

Pre‑built workflows, AI models, and compliance for each specialty—launch faster and safer.

Radiology & Imaging

  • Triage flags, zero‑click CAD, auto‑measurements

Pathology

  • WSI slide QC, mitosis detection, molecular stain prediction

Cardiology

  • ECG arrhythmia ML, echo strain analytics, FHIR waveform ingest

Ophthalmology

  • DR grading, OCT segmentation, mobile fundus AI

Population Health

  • Predictive risk, gaps‑in‑care stratification, SDOH fusion

Accelerator kits trim validation & deployment time 40–60 % per specialty.

AI‑Diagnostics Maturity Curve

From pilot to autonomous AI, each step powered by robust data & compliance.

Pilot

KPI Ceiling

One model, single site

Blockers

Data silos

VarenyaZ Accelerator

FHIR Lake Ingest in 6 wks

Integrated

KPI Ceiling

PACS overlay

Blockers

Workflow lag

VarenyaZ Accelerator

Edge Inference + Smart Cache

Validated

KPI Ceiling

FDA 510(k) clearance

Blockers

Doc bundles, traceability

VarenyaZ Accelerator

SaMD Validation Toolkit

Adaptive

KPI Ceiling

Auto‑retrain on new data

Blockers

Drift, bias

VarenyaZ Accelerator

Bias‑Sentinel & CI CD4ML

Autonomous

KPI Ceiling

Full multimodal AI MDT

Blockers

Culture

VarenyaZ Accelerator

Human‑in‑the‑Loop UX & guardrails

Proven Impact

Median across three 2024 hospital networks.

Stroke CT‑to‑Report

Baseline:11 min
After VarenyaZ:3.9 min
– 64 %

Cancer Detection (PPV)

Baseline:0.31
After VarenyaZ:0.46
+ 15 pts

Missed Findings / 1 k

Baseline:17
After VarenyaZ:6
– 65 %

Clinician Clicks/Study

Baseline:47
After VarenyaZ:23
– 51 %

Cost per Read

Baseline:$48
After VarenyaZ:$32
– 33 %

Signature Case Story — Oncology Network

AI for lung‑nodule CT flagged subtle lesions invisible in 2 D slices.

Results:

  • p95 inference 140 ms
  • Stage 1 detections + 22 %
  • Radiologist TAT – 58 %
  • 510(k) clearance in 7 months

Partner Ecosystem

Proven AI, cloud, and PACS/EHR integrations for frictionless deployments.

Amazon Web Services logo
Microsoft Azure logo
Google Cloud Platform logo
Snowflake logo
Databricks logo
Microsoft Power BI logo
Neo4j graph database logo
Fivetran logo

Ready to Diagnose Faster, Safer, Smarter?

Book a 30‑minute AI‑diagnostics consult — receive a latency audit, validation roadmap, and ROI model, free.

VarenyaZ — data to diagnosis, without delays or doubt.

Frequently Asked Questions

Everything you need to know — or just ask us directly.

How long to deploy AI CT triage?

What is the FDA path?

Can we validate without PHI leakage?

GPU cost concerns?

Explainability for radiologists?

How do you handle bias?

What about HL7 V2 labs?

Edge vs. cloud inference?

Can we plug in third‑party models?

Data residency for EU centers?

Does XAI slow inference?

Do you support non‑imaging AI (ECG, EEG)?

How is clinician feedback looped?

What uptime can we guarantee?

Who owns models & code?

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