Abstract
Telemedicine over Internet of Things (IoT) generates an unprecedented amount of data, which further requires transmission, analysis, and storage. Deploying cloud computing to handle data of this magnitude will introduce unacceptable data analysis latency and high storage costs. Thus, mobile edge computing (MEC) deployed between the cloud and users, which is close to the nodes of data generation, can tackle these problems in 5G scenarios with the help of artificial intelligence. This paper proposes a telemedicine system based on MEC and artificial intelligence for remote health monitoring and automatic disease diagnosis. The integration of different technologies such as computers, medicine, and telecommunications will significantly improve the efficiency of patient treatment and reduce the cost of health care.
Highlights
Telemedicine is an emerging mode of modern medical services, which can meet the real-time monitoring and health management of people’s daily health at home, and greatly relieve the pressure of outpatient visits in hospitals
Various medical sensors and wearable biomedical devices can collect real-time patient health indicators, including body temperature, heart rate, blood pressure, blood glucose, and electrocardiogram, among others. These devices can be connected to the cloud; the latter performs Artificial Intelligence (AI) analysis of patient health-related data, records the patient’s health status, provides disease analysis for doctors and patients, and provides auxiliary decision-making for patient treatment
The evolution of mobile technology is a key part of the overall development of machine-to-machine communication and Internet of Things (IoT). 5G technology will have a significant impact on many telemedicine scenarios
Summary
Telemedicine is an emerging mode of modern medical services, which can meet the real-time monitoring and health management of people’s daily health at home, and greatly relieve the pressure of outpatient visits in hospitals. Various medical sensors and wearable biomedical devices can collect real-time patient health indicators, including body temperature, heart rate, blood pressure, blood glucose, and electrocardiogram, among others These devices can be connected to the cloud; the latter performs AI analysis of patient health-related data, records the patient’s health status, provides disease analysis for doctors and patients, and provides auxiliary decision-making for patient treatment. It uses IoT devices to monitor patients in smart home environments It implements event classification based on fog computing for real-time response and provides real-time decision-making information to doctors and caregivers in various situations. Other than the previous work, our proposed system can provide users with remote medical services through IoT, MEC, and machine learning technologies by monitoring the user’s physical indicators in real time and predicting the patient’s health.
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