“Cloud computing is a great euphemism for centralization of computer services under one server.”
Evgeny Morozov, American researcher
In past MI10 newsletters, we discussed emerging technologies with relevance to artificial intelligence: digital twins/threads, federated/swarm learning, and new advances in deep learning. This week we discuss an innovative technology that will be impactful in clinical medicine and healthcare. While the cloud has thus far been the repository of data and AI for insights from the data, the exponential rise of connected IoT devices and the generated data has surpassed the current capabilities of the network and infrastructure.
Edge computing is a distributed computing framework or architecture that will bring applications closer to data sources (IoT devices or local servers) so that data will be collected and analyzed in proximity to the data source. This closer coupling to the data sources can decrease response times and increase insight velocity as well as lessen the bandwidth demand of a centralized data repository.
Karim Arabi, the CEO of Atlazo, explained that cloud computing is focused on big data while edge computing is focused on real-time “instant data” from devices and sensors. A clinician can think of edge computing as the “peripheral nervous system” with its neurons that is capable of sending signals to the central nervous system or brain (the cloud).
This edge computing architecture has future implications for innovations in clinical medicine and healthcare. First, real-time analytics for certain critical care healthcare venues such as intensive care units, operation rooms, and emergency departments can be advantageous as minimizing latency is essential.
In addition, the surge of wearable devices as well as healthcare sensors will benefit from not only real-time analytics in these edge devices but also data filtering that will be needed from the volume of data.
Lastly, swarm learning that will allow centers to share insights without sharing data will need blockchain as well as edge nodes and computing for its execution. While federated learning keeps data at the edge, swarm learning will keep data as well as the parameters at the edge, thus obviating the need for a central custodian.
All three aforementioned use cases of edge computing are essential for the future of healthcare.