Billion Labs offers a platform that converts ordinary smartphones into medical‑grade devices through a downloadable app, enabling users to capture and analyze health signals with clinically validated digital tools. Their solution leverages biosignal processing and machine learning to provide accurate measurements for a range of applications, from cardiac monitoring to surgical support, making advanced health monitoring accessible to the masses.
Funding
Funding not disclosed
Founders
Product
Problem
Access to clinically validated diagnostic tools is limited by the need for specialized, expensive equipment and in‑person visits, preventing many individuals from regularly monitoring their health. This gap reduces early detection of conditions that could be identified through continuous biosignal analysis. Consequently, consumers lack convenient, reliable ways to capture medical‑grade data using devices they already own.
Solution
Billionlabsinc offers a mobile platform that converts standard smartphones into medically certified diagnostic devices through a downloadable application. The app leverages the phone’s built‑in sensors and optional peripheral attachments to record biosignals such as heart rhythm, blood oxygen, and respiratory patterns. Advanced machine‑learning models process the raw signals to generate clinically validated health metrics and alerts directly on the device. Results are encrypted and can be synced to cloud services for longitudinal tracking or shared with healthcare providers via secure links. By delivering accurate diagnostics without additional hardware, the solution enables continuous, at‑home health monitoring for a broad consumer base.
Target Audience
Primary users are health‑conscious consumers who want regular, medical‑grade monitoring of vital signs, as well as telehealth providers and clinicians seeking scalable, patient‑generated data for remote assessment.
Features
- Utilizes smartphone cameras, microphones, and motion sensors to capture ECG, PPG, and respiratory signals without external hardware
- Clinically validated algorithms that meet regulatory standards for diagnostic accuracy
- On‑device machine‑learning pipelines that analyze raw biosignals and produce actionable health metrics in real time
- Cross‑platform mobile app (iOS and Android) with a unified user interface for signal acquisition, visualization, and trend analysis
- End‑to‑end encryption for data storage and transmission, ensuring privacy and compliance with health data regulations
- Cloud‑based longitudinal database that aggregates measurements for personalized health insights and optional provider sharing
- API hooks for integration with electronic health record (EHR) systems and telehealth platforms