Keeping people healthy
Local AI can make healthcare more accessible, affordable and accurate
From patients to providers, local AI can help reduce healthcare costs by enabling more accurate diagnoses, preventing accidents, and improving compliance. Edge devices can also extend healthcare to underserved populations with limited medical infrastructure.
It is predicted that there will be $150B in savings in the US healthcare economy alone by 2026 across a range of applications, from robot-assisted surgery and virtual nursing assistance to dosage error reduction and automated image diagnosis.
Imagine patient monitoring around the clock, while maintaining privacy.
At large hospitals, patients under care always outnumber doctors and nurses, making 1:1 monitoring impossible
ML-enabled cameras and sensors could allow medical staff to be alerted to events of their choosing, such as falls, lack of movement, or other kinds of behavior.
Because we provide local AI, no sensitive data ever leaves the device — only the critical alerts do, keeping patient privacy intact while improving care.
Imagine earlier detection of deadly diseases — at scale.
Medical imaging creates many more images than there are doctors to diagnose them. In the next few years, image classification models will help doctors to diagnose everything from cancer to stroke more quickly and accurately.
These models combine the ability to detect color shades and patterns the human eye cannot discern and a historical knowledge of disease outcomes. They can help medical professionals catch signs they might have missed, while doing so at enormous scale.
Devices with local AI can also act as filters to help doctors deep dive on the important parts of scans and imaging, using their time more efficiently.
Imagine eyesight-saving scans that don’t require an expert to perform.
Blindness is a very real possibility for the 500 million diabetics worldwide. Today, screening for diabetic retinopathy, which causes blindness, can only be done by specialists.
Our AI solution can be used to build a diagnostics device that captures images of the lining of the eyeball and then runs inferences locally, on off-the-shelf hardware. That means primary care clinics could run these crucial screenings, making them more accessible to the patients who need them.
One key component in elder care is ensuring medication is taken on schedule. In fact, the annual cost for missed medications is estimated to be up to $300 billion in the India alone.
By running multiple AI models such as pose and gaze detection—and even medicine recognition—our AI-enabled at-home device could not only remind patients to take their medications but also dispense the prescribed dose on schedules set by a doctor, while ensuring compliance.
Additionally, continued activity monitoring can allow for disease detection and prevention while allowing people to age in place.