CLINICAL_AI
HealthcareImaging AI
Workflow-integrated deployment with MONAI Deploy. DICOM/FHIR interoperability, PACS integration, and hospital-ready AI applications.
✓
Regulated, auditable clinical workflows
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On-prem, PHI-compliant deployment
✓
MONAI Deploy Platform + Express
LIVE_METRICS
STUDIES_PROCESSED
1,247
P95_LATENCY
42.3s
DICE_SCORE
0.912
COMPLIANCE
100%
Real-time monitoring • DICOM round-trip • PHI-secure
DIAGNOSIS
Hospitals struggle to move AI from "a research model on a laptop" into regulated, auditable clinical workflows
Point models without a deployment backbone stall at the last mile, so radiologists never see value at the workstation.
INTEGRATION_BARRIERS
DICOM/FHIR Interoperability
PACS/RIS Integration
On-Prem Constraints
Privacy & PHI Security
Auditability & Versioning
IT Security & Governance
End-to-End Clinical Integration
Packaging, routing, and orchestrating models so imaging AI runs inside hospital networks, speaks DICOM/FHIR, returns structured results to PACS/VNA/OHIF, and is monitored, versioned, and reversible under governance.
MONAI Deploy
Hospital-grade deployment stack from Project MONAI
COMPONENT
REPOSITORY
PURPOSE
App SDK
monai-deploy-app-sdk
Build/packaging of clinical AI apps (MAPs)
Informatics Gateway
monai-deploy-informatics-gateway
DICOM/FHIR I/O and event publishing
Workflow Manager
monai-deploy-workflow-manager
Orchestration and routing of MAPs
Deploy Express
monai-deploy (Express)
Turnkey pilot for rapid clinical integration
Not a model zoo —
the clinical AI substrate
the clinical AI substrate
By standardizing packaging (MAP), I/O (DICOM/FHIR), and orchestration, MONAI Deploy lets us stand up auditable, governed, on-prem AI services that radiologists actually use.
1
2025 Platform Releases
Documented clinical workflows and Express tooling
2
Field Demonstrations
End-to-end workflows from PACS to workstation
3
Hospital-Grade Stack
Open, actively maintained, production-ready
Clinical Architecture
Clinic-Ready Workflow
PACS/VNA/RIS
→
Informatics Gateway
DICOM/FHIR I/O • Event publishing
↓
Workflow Manager
Orchestration • Routing rules
↓
MAP #1 - Segmentation
e.g., liver tumor
MAP #2 - Classification
e.g., breast density
MAP #3 - Detection
e.g., nodule detect
↓
DICOM RESULTS → PACS/OHIF + JSON/FHIR
Gateway
Handles DICOM/FHIR ingress/egress and notifies orchestrator
Workflow Manager
Schedules MAPs per study/protocol rules
MAPs
Containerized apps with clear inputs/outputs & validation hooks
Technical Integration
DATA_INTEROPERABILITY
Modalities
CT, MR, X-ray, MG, US - multi-series handling
Payloads
Anonymized or full PHI (on-prem)
Outputs
DICOM SEG/SC, RTSTRUCT, or JSON with FHIR Observation
PACKAGING_REPRODUCIBILITY
1
MAP (MONAI Application Package)
Self-describing container with operators
2
Interface Contracts
Declared inputs/outputs and test scaffolding
3
Versioning
Image tags + config hashes, promoted dev → test → prod
Orchestration & Scaling
→ Routing rules: modality, body-part, accession, StudyInstanceUID
→ Parallelism: concurrent MAP runs per node, GPU pinning
→ Queueing: back-pressure & retry, poison-queue isolation
→ Batching: per series or study, configurable memory profiles
→ Metrics: latency per stage, GPU/CPU usage, success rate
→ Audit: full provenance of inputs, operator graph, model version
Deployment Patterns
01
Pilot (Express) → Production (Platform)
Express instance for rapid path-to-value
Orthanc + turnkey orchestration
Verify end-to-end clinical loop (2025 documented)
Migrate to full MONAI Deploy with HA, SSO, logging
02
On-Prem GPU Cluster
Kubernetes + NVIDIA runtime
MAPs as pods with GPU pinning
Node taints for PHI isolation
Horizontal autoscaling based on study volume
03
Edge Nodes (Satellite Sites)
Gateway on site, burst to central cluster
Store-and-forward if offline
Local caching for low-latency
Periodic sync with central registry
Clinical Blueprints
CASE_A
Cardiac CT Total Volume
Triage/Quantification
A
GOAL
Automatically compute total cardiac volume on chest CT and surface quantitative series to radiologist
BUILD
•Package TorchScript model into MAP with operators
•DICOM import → resample → segment heart & chambers
•Compute volumes → export DICOM SEG + SC overlay
•Route via Workflow Manager when modality=CT and body-part=Chest
•Return results to PACS/Orthanc; verify in OHIF
KPIs
Dice ≥ 0.90 on test cohort • P95 latency < 60s per study • 0 routing failures over 1k studies
CASE_B
Liver Tumor Segmentation
Treatment Planning
B
GOAL
Create DICOM SEG masks with lesion volumes and counts for MDT review
BUILD
•Adapt ai_livertumor_seg_app example into site MAP
•Add QC overlay and PDF summary
•Export FHIR Observation with total volume
•SDK examples show liver/pancreas/spleen apps
KPIs
Dice (lesion) ≥ 0.80 on internal test • Structured report round-trip < 90s • Automated reprocess on failure
CASE_C
Breast Density Classification
Screening Workflow
C
GOAL
Derive BI-RADS density category on MG and attach to study as Secondary Capture with JSON
BUILD
•Use SDK classification example as base (breast density app)
•Enforce deterministic preprocessing
•Archive JSON to VNA
KPIs
AUC ≥ 0.90 • Disagreement analysis vs reader consensus • Latency < 30s per study
ALGORITHM_PATTERNS
Segmentation
Classification
Detection
Quantification
Post-Processing
SDK examples include spleen, pancreas, liver tumor segmentation — ready to package and adapt as MAPs
Evaluation & MLOps
CLINIC_GRADE_EVALUATION
Technical
Series acceptance
Inference latency (P95/P99)
Throughput at study volume
GPU utilization
Clinical
Sensitivity/specificity
ROC-AUC/PR-AUC
Dice/HD95 for segmentation
Time-to-read delta
Operational
Round-trip (PACS→AI→PACS)
Failure rate
Reprocessing success
Anonymization coverage
Governance
Drift monitors (distribution, calibration)
Version performance tracking
Audit trail completeness
MLOPS_INFRASTRUCTURE
Registry
→ Checkpoint + shard plan
→ Config hashes
→ Promotion gates
CI/CD
→ Golden-set reprocessing
→ Statistical tests
→ Blue/green deploy
Monitoring
→ KPI dashboards per service
→ Latency/timeout alerts
→ GPU/CPU metrics
Rollback
→ Traffic shift on KPI breach
→ Previous MAP version
→ Immutable logs
Safety, Privacy & Compliance
CONTROL
IMPLEMENTATION
PHI Boundary
On-prem inference; no data egress
Access Control
LDAP/OIDC for services; signed containers
Data Minimization
Series-level filters; de-identification operators
Standards Alignment
DICOM C-STORE/C-FIND; FHIR Observation; IHE AIW-I
Validation
Golden-set reprocessing; statistical acceptance tests
COMMITTED_KPIS
Reliability
End-to-end success rate
≥ 99.5%
Latency
P95 study round-trip
≤ 60 s
Quality
Dice (organ/lesion)
≥ 0.85/0.80
Ops
Reprocess auto-recovery
≥ 99%
Governance
Full provenance coverage
100% of studies
Security
External egress of PHI
0 by policy
From PyTorch
to the
Reading Station
Not a model zoo — the clinical AI substrate. MONAI Deploy standardizes packaging, I/O, and orchestration for auditable, governed, on-prem AI services.
SUCCESS_RATE
99.5%
P95_LATENCY
<60s
DICE_SCORE
≥0.85
PHI_EGRESS
ZERO
MONAI Deploy Platform • App SDK • Informatics Gateway • Workflow Manager • Express