Diagnostics & imaging AI · Automated echo reporting
Us2.ai
Us2.ai makes AI software that analyzes a full echocardiogram, measures heart structure and function against international guidelines, and drafts the echo report for cardiologists to review.
Strong fit
- Hospital echo labs with long reading backlogs and many sonographers
- Cardiology groups that want guideline-based measurements done the same way on every study
- Health systems screening for heart failure and cardiac amyloidosis at scale
Weak fit
- Sites that read only a handful of echoes a week
- Buyers who want AI for CT, MRI, or X-ray in the same purchase
- Buyers who need a published price list
Bottom line
Us2.ai is worth using for echo labs that want faster, more consistent reports from the studies they run today. Its FDA-cleared AI analyzes a full echocardiogram from any ultrasound machine and returns measurements and a draft report in under two minutes. Labs focused on finding missed heart failure may prefer Ultromics.
Score breakdown
Weights: Outcomes 35% · Product 30% · Implementation 20% · Pricing clarity 15%.
Us2.ai, based in Singapore, makes AI software that analyzes a full echocardiogram, the ultrasound scan of the heart.
James Hare, a co-founder of the travel site eDreams, started the company in 2017 as eko.ai with Dr. Carolyn Lam, a heart failure cardiologist at the National Heart Centre Singapore, and Dr. Yoran Hummel, a cardiovascular researcher. It took the name Us2.ai in 2021, when its first fully automated echo tool gained FDA clearance.
The software connects to ultrasound machines from any maker and the PACS, and picks the right frames from each study. It measures the heart's chambers, pumping function, Doppler flow, and strain, and checks each result against international echo guidelines. It returns a complete set of measurements and a draft report in under two minutes.
Sonographers capture images as usual, and cardiologists review and sign the draft report. A separate FDA-cleared model flags likely cardiac amyloidosis from a single standard view.
Sequoia India and EDBI led a $4.5 million seed round in 2019. IHH Healthcare, the hospital group, led a $15 million Series A in 2022, the year the software gained its European CE mark.
Its accuracy was validated in The Lancet Digital Health, and AI-ECHO, a randomized trial of its echo AI, was presented at the American Heart Association meeting.
Fujifilm builds Us2.ai into its ultrasound products, and the company also works with deepc, Harrison.ai, and Merative's Merge imaging software.
At Endeavor Health in Illinois, the AI workflow saved 10 to 12 minutes per study on average, about a third of the time each study took. Advara HeartCare, an Australian cardiology group, has also partnered with Us2.ai.
Compare Ultromics when the main goal is finding missed heart failure in echoes the lab reads today, and AISAP when the scans come from ultrasound at the bedside.
Studycast fits a lab that needs a cloud system for storing, reading, and reporting studies.
Competitor landscape
| Vendor | Overall | Ease of implementation |
|---|---|---|
| Us2.ai | 7.7 | 7.5 |
| Ultromics | 7.4 | 7.1 |
| Studycast | 7.2 | 6.9 |
| AISAP | 7.0 | 6.6 |
Pricing
| Item | Detail |
|---|---|
| Model | Custom contract quoted by Us2.ai, sold directly and through imaging partners such as Fujifilm. |
| What usually drives cost | Study volume, the number of sites and ultrasound machines, how the software connects to the PACS and reporting system, and whether amyloidosis detection is included. |
| What to ask before you buy | Whether pricing is per study or a site license, how it changes when bought through an imaging partner, and what the PACS and reporting connection costs. |
| Published pricing | Us2.ai does not publish a price list. |
Prerequisites for purchase
| Need | Why it matters |
|---|---|
| What you need to get Us2.ai to function | |
| Echo lab medical director | Signs off on the AI measurements. |
| Connection to ultrasound machines and PACS | Studies flow in automatically. |
| Reporting system access | Draft reports land where cardiologists sign. |
| Baseline reading time per study | You need them to show the change later. |
| Sonographers trained on standard views | AI needs complete studies. |
| What will maximize your value | |
| Start with routine adult echoes | High volume and clear guidelines. |
| Track reading time by reader | Shows where time is saved. |
| Add amyloidosis screening after go-live | Builds on the base workflow. |
| Deal-breakers | |
| Few echoes a week | |
| No network path from scanners to the PACS | |
| No cardiologist to review AI output | |
Value creation time frame
| # | Stage | Typical range |
|---|---|---|
| 1 | Contract and PACS connection | 1-3 months |
| 2 | First echo lab live | 1-2 months |
| 3 | More sites and screening programs | 6-12 months |
Leadership
Methodology
| Weight | Factor | What it measures |
|---|---|---|
| 35% | Customer outcomes | Whether buyers get measurable operational or clinical-workflow results after go-live |
| 30% | Product | Capability depth, reliability, and fit for the job the category buys |
| 20% | Implementation | How hard it is to stand up, integrate, train, and stabilize |
| 15% | Pricing clarity | Whether a buyer can model total cost without a mystery quote |
| Label | Meaning |
|---|---|
| Highly recommend | Strong outcomes and product with manageable caveats |
| Recommend | Solid fit for the right buyer; know the tradeoffs |
| Conditional | Only with a specific use case or heavy caveats |
| Not recommended | Avoid for most buyers in this category |
Read our full methodology for how we weight scores and assign recommend labels.