University of Notre Dame Australia (PANACEA-HF program) · Us2.ai
zero — Imaging failure rates
Vendor-reportedHealthcare · Diagnostics · Computer vision · Pilot
Diagnostics
University of Medicine and Pharmacy Hospital, Ho Chi Minh City · Us2.ai
Vendor-reportedHealthcareVietnamPilotPublic sectorComputer vision
Vendor-reported. The customer is named and the numbers are quoted from the source page, but the account comes from the vendor. No independent confirmation.
Reported by us2.ai (vendor self-report). Checked against the source page on 2026-09-06. 1 of 1 figures below appear on that page word for word.
The problem
Manual measurement of echocardiograms is labor-intensive and creates a bottleneck in diagnosis time in a high-volume unit.
What was deployed
- Vendor
- Us2.ai
- Products
- Us2.ai
- Technique
- Computer vision
- Build or buy
- Bought off the shelf
- Data used
- standard DICOM 2D echocardiograms
- Scale
- Over 120 studies were analyzed
What changed
Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.
about 10 minutesdiagnosis timefrom 35 to 40 minutes
“AI can significantly shorten diagnosis time from 35 to 40 minutes down to about 10 minutes.”
Quoted word for word from the source
easesworkload for medical staff
“This innovation not only eases the workload for medical staff but also minimizes errors and reduces treatment costs.”
Quoted word for word from the source
minimizeserrors
“This innovation not only eases the workload for medical staff but also minimizes errors and reduces treatment costs.”
Quoted word for word from the source
reducestreatment costs
“This innovation not only eases the workload for medical staff but also minimizes errors and reduces treatment costs.”
Quoted word for word from the source
“AI can significantly shorten diagnosis time from 35 to 40 minutes down to about 10 minutes. This innovation not only eases the workload for medical staff but also minimizes errors and reduces treatment costs.”
Difficulties and limits
- This is a clinician's reported observation at a single site during an initial deployment, and it is worth reading as exactly that rather than as a specification. The figure describes the time to reach a diagnosis in that unit's workflow, it is given as an approximation, and it has not been published as a measured endpoint in a peer-reviewed study.
Sources
- us2.ai — vendor self-report
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