Medical AI company
Ultromics
University of Oxford spin-out developing FDA-cleared AI that analyzes routine echocardiograms to help identify HFpEF and cardiac amyloidosis.
Company overview
Ultromics develops EchoGo, AI software that analyzes routine echocardiograms within existing workflows to help clinicians identify heart failure with preserved ejection fraction (HFpEF) and cardiac amyloidosis. The company originated at the University of Oxford and states its AI is deployed in US hospitals, with a CPT code assigned for EchoGo Heart Failure. Partners named on its site include Mayo Clinic and Pfizer.
- Headquarters
- United Kingdom
- Company type
- AI Technology Developer
- Status
- Established Company
In its own words: “Ultromics develops AI that helps clinicians surface clinically relevant findings that may otherwise go unrecognized, supporting more informed diagnostic pathways from one of the world's most widely used cardiac imaging tests.” source
AI products and platforms
- EchoGo Heart FailureAI-powered
AI diagnostic aid that identifies HFpEF from a routine echocardiogram.
Product page ↗ - EchoGo AmyloidosisAI-powered
AI screening aid that flags findings suggestive of cardiac amyloidosis from echocardiograms.
Product page ↗
Technology categories
Clinical applications
- echocardiography interpretation
- HFpEF detection
- cardiac amyloidosis screening
Markets served
- United States
Relationships
- Partner Mayo Clinic source
Relationships are shown as stated on the cited page; the other party may not confirm them.
Regulatory information
- EchoGo Heart Failure FDA 510(k) cleared as a diagnostic aid; EchoGo Amyloidosis FDA 510(k) cleared as a screening aid (K240013, K240860 referenced) source
- FDA Breakthrough Device Designations (two) per company site source
As stated by the company or the cited regulator page. Authorisation is specific to a product, version and market; confirm the current status with the company and the regulator. About medical device regulation
Official sources
Last verified 29 October 2026 against the sources above.
Questions about Ultromics
Does Medical AI have to explain how it reaches its outputs?
Not always in a technical sense, but transparency about the product is increasingly expected. Many machine learning models cannot give a simple step-by-step reason for each output, so regulators focus on clear information for users: intended use, inputs, how the model was developed and tested, performance, and known limitations.
In the US, one of the criteria for clinical decision support to fall outside device regulation is that a healthcare professional can independently review the basis for its recommendations. FDA has also published transparency principles for machine learning devices with Health Canada and MHRA.
For buyers, a vendor's ability to document how its model works and where it fails is often more useful than heat maps or confidence scores alone.
How is AI used with ECGs from wearables and remote monitors?
AI is used to screen large volumes of ECG data from patches, wearables and remote monitors, flagging possible arrhythmias or recordings that need clinician review. This helps manage the large amount of data produced by continuous monitoring.
Important distinctions for buyers:
- A consumer feature on a wearable and a clinical-grade analysis service can have very different intended uses and authorizations.
- Authorization often covers specific recording hardware, leads and populations.
- Signal quality and motion artifacts affect results.
In the US, software that analyzes physiological signals does not meet the criteria for non-device CDS. Confirm each product's status, and check how alerts are routed to clinicians and documented.
What is AI image reconstruction and denoising?
AI image reconstruction and denoising use deep learning to produce clearer images from raw scanner data or to reduce noise and artifacts, for example in CT or MRI. Vendors often position these tools as supporting lower-dose or faster scanning protocols.
Unlike detection software, these tools change the image the clinician reads rather than adding a finding. Questions to ask include:
- Is the function built into the scanner or a separate software product?
- Which scanners, protocols and body regions is it authorized for?
- How was diagnostic image quality evaluated, and could fine detail be lost or altered?
These functions are typically regulated as part of the imaging device or as image processing software, depending on the product and market.