Medical AI company
AliveCor
Maker of Kardia personal and clinical ECG devices with FDA-cleared AI algorithms that detect cardiac rhythms and morphologies.
Company overview
AliveCor develops Kardia ECG devices, including KardiaMobile, KardiaMobile 6L and the handheld Kardia 12L system, along with software such as KardiaPro and KardiaStation. Its deep neural network algorithms interpret ECGs and, per the company, detect up to 35 FDA-cleared rhythm and morphology determinations. It serves consumers, health systems, providers, payers and biopharma.
- Headquarters
- United States
- Company type
- AI Technology Developer, Medical Technology Manufacturer
- Status
- Established Company
In its own words: “We’re transforming the industry with clinically validated, AI-powered solutions that help democratize access across the continuum of cardiac care—from screening and diagnosis to treatment and management.” source
AI products and platforms
- Kardia 12LAI-powered
Portable, AI-powered handheld 12-lead ECG system using a reduced lead set.
Product page ↗ - KardiaMobile 6LAI-powered
Personal six-lead ECG device with AI rhythm determinations.
Product page ↗
Technology categories
Clinical applications
- ECG acquisition and interpretation
- arrhythmia detection
- remote cardiac monitoring
Markets served
- United States
Regulatory information
- Kardia detects a set of 35 FDA-cleared determinations (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 AliveCor
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 automation bias, and why does it matter for clinical AI?
Automation bias is the tendency of people to over-rely on a suggestion from an automated system, either following an incorrect output or missing something because the system did not flag it.
FDA's Clinical Decision Support Software guidance (issued January 2026) discusses automation bias and notes it increases in urgent situations where there is not enough time to consider other information. This is one reason FDA does not consider software intended for critical, time-sensitive decisions to meet the criteria for non-device CDS.
Organizations reduce automation bias through user training, clear display of the basis and limits of an output, workflows that keep independent clinical review, and monitoring of how the tool is actually used.