Samay vs VUNO (2026)
Two very different bets on where clinical signal comes from. Samay builds a chest worn device that projects low frequency sound into the lungs and reads what returns, creating a respiratory measurement that currently requires a pulmonary function laboratory, and no clearance is evidenced for it. VUNO predicts in hospital cardiac arrest within a day from four vital signs the ward already charts, with external multicenter validation behind it and nothing new to buy. For a hospital today, VUNO is deployable and Samay is a technology to watch. The question Samay must eventually answer is whether its acoustic signal quality varies with body habitus, which is the same class of problem as pigmentation in optical measurement and equally unpublished.
- The sensing method is novel, projecting low frequency sound into the lungs and analyzing the returning signal, so it creates a measurement that does not otherwise exist outside a pulmonary function laboratory.
- Its evidence is appropriately caveated, including a study with a defined patient population.
- Scope is narrow and clearly stated, respiratory disease specifically, rather than a general monitoring claim.
- It predicts in hospital cardiac arrest within a day from four vital signs the ward already charts, so nothing new has to be bought or worn.
- External multicenter validation across several tertiary hospitals speaks to how it holds up from site to site.
- Inputs and outputs are stated precisely, four named vital signs producing a bounded score over a defined horizon.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. Samay and VUNO are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI Health Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Fact | Samay | VUNO |
|---|---|---|
| Primary category | Remote Monitoring & Chronic Care | Clinical Decision Support |
| Founded | 2018 | 2014 |
| Headquarters | Mountain View, California, United States | Seoul, South Korea |
| Website | samayhealth.com | vuno.co |
Side by Side
Select any grade to read the note behind it.
Product updates
The latest logged changes from each vendor, newest first, each linked to its source.
VUNO
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Clinical evidence
Researchers from VUNO published a peer-reviewed study in the American Journal of Neuroradiology validating a machine learning algorithm that uses automated brain MRI volumetry to differentiate frontotemporal dementia from Alzheimer's disease.
Source
Each vendor in one paragraph
Written to be quoted whole. Each paragraph opens the AI Health Index profile of the vendor: what the product is, who buys it and what it does.
Respiratory diagnostics company developing Sylvee, a chest worn wearable that projects low frequency sound into the lungs and analyzes the returning resonance with machine learning to measure lung volumes, trapped air, respiratory rate and breathing sounds, positioned for early COPD detection, continuous home monitoring of COPD and small airway disease, and endpoint capture in pharmaceutical trials.
Source: AI Health Index, September 2026
VUNO, a Seoul medical AI company founded in 2014, leads with DeepCARS, which predicts in-hospital cardiac arrest within 24 hours for patients on general wards from four routinely charted vital signs: blood pressure, heart rate, respiratory rate and temperature. It returns a 0 to 100 risk score for ward staff and rapid response teams to act on, and the company describes it as Korea's first AI medical device for this purpose.
Source: AI Health Index, September 2026
Questions buyers ask
How do Samay and VUNO handle patient data?
For Samay, the question specific to this device is audio. Sylvee projects sound into the chest and reads the returning resonance with speakers and microphones, is worn taped to the chest for continuous home use, and the company lists breathing sounds among its metrics.
With VUNO, the policy does not say whether information processed for a hospital is pseudonymized and used to develop the company's models, or whether a hospital can decline that use. From the AI Health Index, drawn from each vendor's own published materials and the regulatory record, verified September 28, 2026. No vendor pays for placement.
What evidence is published for Samay and VUNO?
In Samay's case, the company's 2025 abstract in the American Journal of Respiratory and Critical Care Medicine reports a COPD classifier on 218 subjects (107 COPD, 111 healthy, mean age 62), validated on 66 held out subjects: area under the curve 0.93, sensitivity 81 percent, specificity 83 percent against conventional pulmonary function tests.
For VUNO, the multicenter validation of 173,368 patients, published in Resuscitation in 2021 and cited on the DeepCARS page, reports an area under the curve of 0.860 internally and 0.905 externally. From the AI Health Index, drawn from each vendor's own published materials and the regulatory record, verified September 28, 2026. No vendor pays for placement.
How much do Samay and VUNO leave to the clinician?
With Samay, results are surfaced through a companion app and a research portal for pulmonologists and primary care physicians to review, and the stated intent is to let clinicians intervene early rather than to automate a decision. Because the device is in development and not cleared, its output today sits in a research or monitoring context.
In VUNO's case, the score runs from 0 to 100 for the risk of cardiac arrest in the general ward within 24 hours, and the company positions the tool as enabling timely intervention rather than triggering automated escalation; nursing staff, not the algorithm, mobilize the response. From the AI Health Index, drawn from each vendor's own published materials and the regulatory record, verified September 28, 2026. No vendor pays for placement.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Remote Monitoring & Chronic Care page.
These sit at different stages and that should not be blurred: one is a device with no clearance evidenced, the other is a deployed model with published multicenter validation. The device raises a specific unanswered question about whether signal quality and model accuracy vary with body habitus, which is the acoustic equivalent of the pigmentation problem, and no subgroup data is published. VUNO's validation is Korean, so a United States buyer should ask what performance looks like in a population with different baseline vital sign distributions and escalation practices.