Volume Outpacing Staffing
CT, MRI, and ultrasound volumes keep climbing while radiologist capacity stays flat — creating backlogs that delay critical findings.
Imaging Intelligence
AI-powered imaging
for faster clinical decisions
Deployable imaging AI for radiology and specialty clinical programs — from CT hemorrhage detection to ultrasound analysis, imaging QA, and AI-integrated review portals that fit into existing workflows.
The Challenge
Why radiology teams need imaging AI now
Rising volumes, staffing gaps, and fragmented tooling create compounding pressure on imaging departments.
Missed Priority Windows
Manual triage relies on requisition data, not actual image content. Time-sensitive findings can sit in routine queues for hours.
Inconsistent QA Standards
Image quality checks are subjective and operator-dependent. Artifacts pass through to reporting, causing retakes and delayed diagnoses.
Disconnected Tooling
AI results live in separate systems from PACS. Radiologists context-switch between platforms instead of reviewing in one unified workflow.
Service Breakdown
What we deploy for imaging teams
Each capability is built for real clinical conditions, validated against operational data, and integrated into production reading workflows.
01
Critical Finding Triage
AI models surface time-sensitive findings — hemorrhages, mass effects, critical fractures — and escalate them to the top of the reading queue before a radiologist even opens the study.
Sub-second inference on CT Head, Chest, and C-Spine studies. Confidence-scored prioritization with configurable escalation thresholds and automated routing to on-call staff.
AI Triage Worklist2 critical
6 studies · sorted by score6
Pending review
2
Escalated
1
Flagged
0.62
Avg score
Study IDPatientModalityAI ScorePriorityStatus
CT-0491M. TorresCT HeadStatEscalated
0.96
CT-0487L. ChenMRI BrainStatEscalated
0.91
CT-0489R. KimCT ChestUrgentFlagged
0.83
CT-0485S. ParkCT PelvisUrgentPending
0.61
CT-0483J. WilliamsUS AbdoRoutineNormal
0.22
CT-0480A. NguyenCT HeadRoutineNormal
0.18
Escalation rule
Score > 0.80 → auto-escalate to Stat queue · Score 0.55–0.80 → Urgent flag for radiologist review
02
Specialty Ultrasound Intelligence
Longitudinal ultrasound analysis for specialty programs like hemophilia care — tracking effusion volumes, synovial thickness, and joint health across visits to catch progression early.
Multi-joint assessment with automated volume measurement, visit-over-visit delta tracking, confidence scoring, and clinician-facing trend dashboards built for point-of-care review.
EffusionAI AnalysisSession 47
Patient: H. Martin · Visit 65
Joints assessed
2
Elevated effusion
90.0%
Avg confidence
Hemophilia
Protocol
Joint Effusion Measurements
Right Knee
Left Knee
Right Hip
Left Ankle
Left Hip
Longitudinal trend
Right knee · last 6 visits · volume trend
03
Imaging QA Automation
Automated quality checks catch motion artifacts, positioning errors, and exposure issues at the point of acquisition — reducing retakes and protecting downstream diagnostic accuracy.
Multi-criteria evaluation across motion blur, exposure consistency, anatomical positioning, and field-of-view compliance. Batch processing with pass/fail/warn classification per study.
QA Artifact CheckerBatch #88
7 studies processed3/7
Passed
2
Failed
2
Warnings
43%
Pass rate
IDTypeMotionExposurePositionResult
XR-0201PA Chest
Pass
XR-0202AP Pelvis
Fail
XR-0203Lateral Spine
Warn
XR-0204PA Chest
Pass
XR-0205AP Shoulder
Fail
XR-0206Dental PA
Pass
XR-0207AP Ankle
Warn
04
AI-Integrated Review Portals
PACS-centered review portals that centralize AI outputs alongside clinical context — so radiologists read smarter, not harder, with AI findings embedded directly into their existing workflow.
DICOM/PACS/RIS integration, role-based access, AI finding overlays, structured reporting templates, and real-time sync with existing reading room infrastructure.
aiPACS Review PortalConnected
PACS · RIS sync active5
Pending reads
2
AI flagged
38
Today reads
#PatientStudyAssignedAI Finding
1042R. Kim
CT · Head
Dr. PatelHemorrhage
1041L. Martinez
MRI · Spine
Dr. Nguyen—
1040J. Park
CT · Chest
Dr. PatelEffusion
1039A. Singh
US · Abdo
Dr. Wu—
1038M. Torres
CT · Pelvis
Dr. Nguyen—
Integration status
DICOM
PACS
RIS
SSO
Delivery Framework
How we deploy imaging AI
A proven four-phase process from clinical discovery to monitored production, designed to minimize disruption to reading workflows.
Workflow Discovery
Map reading-room operations, escalation rules, and modality-specific requirements with clinical stakeholders.
Validation & Model Readiness
Prepare datasets, validate model behavior, and align outputs to decision-support expectations before go-live.
Integration & Rollout
Connect to DICOM/PACS/RIS pathways and deploy through phased onboarding without disrupting reading habits.
Monitoring & Iteration
Track model and workflow performance, refine thresholds, and evolve deployment with operational feedback loops.
Outcomes
What imaging teams can expect
< 30s
Triage Speed
From study arrival to AI-scored prioritization
96.2%
Escalation Accuracy
Critical finding detection precision
94%
QA Catch Rate
Artifacts caught before radiologist review
4–6 wks
Integration Time
PACS/RIS connection to production
Ready to deploy imaging AI in your environment?
We can map a realistic deployment path around your current systems and constraints.
Get in touch