AI Diagnostic Tool: 3-Hospital Rollout for Chest X-Ray AI
How a regional hospital network deployed an FDA-cleared chest X-ray AI across three facilities in production — what the clinical workflow integration required, and what radiologists and clinicians actually experienced.
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Running an FDA-cleared AI diagnostic in production is nothing like a vendor demo: results must reach the radiologist for every eligible study and hold up across thousands a month. This case study covers the deployment across three hospitals — integration architecture, clinical change management, and six-month outcomes.
Healthcare / Radiology
Hospital radiology departments reading ~85,000 chest X-ray studies a year.
Regional 3-Hospital Network
Three facilities carrying unfilled radiologist positions and rising workload.
FDA-Cleared Chest X-Ray AI in Production
AI output reaches the radiologist workflow on every eligible study, not a pilot subset.
PACS to AI engine and back to the worklist
Studies auto-route at acquisition and findings return as a DICOM Structured Report.
Monthly QA and Threshold Tuning
Dismissed findings are reviewed monthly and confidence thresholds tuned from real reads.
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The Clinical Problem
A high-volume network needed faster, safer chest X-ray reads.
Surface critical findings faster.
Support your radiologists.
Deploy AI that clinicians trust.
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What Made the Deployment Clinically Valuable
Over the first 90 days, the share of radiologists who preferred the AI overlay visible from the start of the read grew as the team built confidence in the tool — the deployment design earned trust rather than demanding it.
Acuity-Driven Worklist
High-confidence pneumothorax studies jumped to the top of the worklist regardless of acquisition time — so the most urgent studies were read first.
Automatic Quality Check
Running on every study, the AI flagged findings not initially in the report; radiologists then confirmed and amended, or dismissed with justification. Leadership called this its most valuable function.
Reduced Cognitive Burden
Radiologists reported lower cognitive load on routine studies and more confidence during high-volume sessions where fatigue could otherwise affect performance.
Radiologist Autonomy Preserved
A flexible workflow let each radiologist choose when to see AI findings, which is why adoption grew steadily instead of meeting resistance.
The Results
Every number below was measured in production after launch — not projected in a pitch deck.
31 min
Urgent ED Report Time — Down from 47 minutes
78%
Radiologists Report Positive Impact — At the six-month survey
85,000
Studies Processed Annually — Across all three facilities
3
Hospitals Live in Production — Network-wide rollout