跳至主要内容

Google Coral could make healthcare more accessible

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.



Accenture predicts $150B in savings in the US healthcare economy by 2026 across a range of applications, from robot-assisted surgery and virtual nursing assistance to dosage error reduction and automated image diagnosis.

PATIENT CARE

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 Coral provides local AI, no sensitive data ever leaves the device — only the critical alerts do, keeping patient privacy intact while improving care.

MORE ACCURATE DIAGNOSTICS

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.

LOW-COST DIAGNOSTICS

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.

Coral 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.

IN-HOME PRESCRIPTION COMPLIANCE

Imagine an elderly patient who never forgets to take the right pills at the right time.


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 US alone.

By running multiple AI models such as pose and gaze detectionmdash; and even medicine recognition—a Coral-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.




If you are interested in Google Coral products, and more details about large volume or bulk sales (Volume Discounts) at #Gravitylink online store, welcome to contact our sales team via email: sales@gravitylink.com or market@gravitylink.com, and we'll get back to you with our best quotation ASAP.

website link : https://store.gravitylink.com/global



评论

此博客中的热门博文

Introducing Google Coral Dev Board

Introducing Google Coral Dev Board (Part 1) Introducing Google Coral Dev Board (Part 2) Introducing Google Coral Dev Board (Part 3) Introducing Google Coral Dev Board (Part 4)

10 questions you should know about Google Edge TPU

1. What is the Edge TPU? The Edge TPU is a small ASIC designed by Google that provides high performance ML inferencing for low-power devices. For example, it can execute state-of-the-art mobile vision models such as MobileNet V2 at almost 400 FPS, in a power efficient manner. Google offers multiple products that include the Edge TPU built-in. Two Edge TPU chips on the head of a US penny 2. What machine learning frameworks does the Edge TPU support? TensorFlow Lite only. 3. What type of neural networks does the Edge TPU support? The first-generation Edge TPU is capable of executing deep feed-forward neural networks (DFF) such as convolutional neural networks (CNN), making it ideal for a variety of vision-based ML applications. 4. How do I create a TensorFlow Lite model for the Edge TPU? You need to convert your model to TensorFlow Lite and it must be quantized using either quantization-aware training (recommended) or full integer post-training quantization. (To create a compatible model...

Introducing Google Coral Edge TPU Device--Mini PCIe Accelerator

Mini PCIe Accelerator A PCIe device that enables easy integration of the Edge TPU into existing systems. Supported host OS: Debian Linux Half-size Mini PCIe form factor Supported Framework: TensorFlow Lite Works with AutoML Vision Edge https://store.gravitylink.com/global/product/miniPcIe The Coral Mini PCIe Accelerator is a PCIe module that brings the Edge TPU coprocessor to existing systems and products. The Mini PCIe Accelerator is a half-size Mini PCIe card designed to fit in any standard Mini PCIe slot. This form-factor enables easy integration into ARM and x86 platforms so you can add local ML acceleration to products such as embedded platforms, mini-PCs, and industrial gateways. https://store.gravitylink.com/global/product/miniPcIe Features Google Edge TPU ML accelerator Standard Half-Mini PCIe card Supports Debian Linux and other variants on host CPU About Edge TPU  The Edge TPU is a small ASIC designed by Google that provi...