How Edge AI is Transforming Healthcare
As a reminder from our previous article in this series, Edge AI is “the implementation of artificial intelligence in an edge computing environment, which allows computations to be done close to where data is actually created, rather than at a centralized cloud computing facility or an offsite data center.”
Distilled down, edge AI computes algorithms on IOT devices (as opposed to in a cloud computing environment), allowing for more efficiency, security, and reliability of the computation, all while saving energy as well through minimizing data transfer to the cloud.
How is this technology transforming healthcare? Let’s explore a few different areas:
Proactive and patient-centric care
Coupling edge computing with AI technology like Emotion AI allows for more personalized, connected, and informed care. According to Gartner, monitoring devices and wearables have the potential to read facial expressions, vitals, and other attributes that shape the picture of a patient’s state, analyze that picture, and then flag the potential need for healthcare provider intervention in real-time. Additionally, both wearables and implants that leverage AI are being studied more and more for their transformative capability of earlier intervention. Smart watches that take passive data to generate an ECG signal have been used in studies to detect heart anomalies, like atrial fibrillation, to inform patients or healthcare providers in real time. Similarly, neural implants coupled with Edge Deep Learning models provided favorable results for earlier seizure detection, and therefore patient outcomes.
Insights in the hands of everyday users
Consider wearables that you may see all the time – Apple Watches, Oura Rings, and more. These harness collected data like your pulse, sleep patterns, and more, and utilize Edge AI and computing to put insights at the hands of the wearer. Additionally, consider the needs of someone who has diabetes – the importance of knowing their blood glucose levels is paramount to their day to day. Continuous glucose monitoring devices, like the one by Know Labs, utilize Edge AI to provide real-time measurements and therefore more proactive management without the need for point-in-time finger pricking.
Speed and security in diagnosis
It is well known that AI is rapidly transforming diagnostics through analyzing images and scans to an advanced precision. Improvement in patient outcomes is palpable when it means earlier detection and, therefore, earlier treatment of many diseases that AI has been trained to identify. Where Edge AI makes its greatest impact is in the security and protection of the massive amount of sensitive data that goes into both training and subsequent analysis. Rather than having to transfer data to a cloud environment for computing, Edge AI allows for powerful computing to be done where the data is collected.
Be on the lookout for our next edition of this series in the next few weeks where we will dive into three new technologies you probably haven’t heard of. If any of these concepts sound interesting, reach out below; we’d love to chat!
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Dayna Larson
is an Associate Principal at Cortico-X with experience in life sciences, consumer products, financial services, and technology sectors.