Purpose – Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for
machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency,
improving availability and saving bandwidth.
Design/methodology/approach – The exponential growth in tensor processing unit (TPU) and graphics
processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology
with deep learning in providing the best patient care. A significant role of pushing and pulling data from the
cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting
the abnormalities and providing customized treatment based on the patient electronic health record (EHR).
Findings – The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to
provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains
importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of
patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and
mobile apps are entrusted with real-time monitoring and diagnosis of patients.
Originality/value – The utility value of sensors data improves through the Laplacian mechanism of
preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and
preservation of the PII values in the local ODL.
Keywords Edge computing, Deep learning, Cloud computing, Fog computing, Electronic health record,
Tensor processing unit, Graphics processing unit, Point of presence