The Role of Edge Computing and 5G in Healthcare

During an epidemic, many people do not want to go to the hospital, which affects their early diagnosis and later recovery. In addition, the steady growth of the elderly population is putting increasing pressure on geriatric care facilities around the world. Technology is changing the way we diagnose, care for and treat patients. Edge computing and 5G are accelerating innovations that will make healthcare faster, cheaper and better. During an epidemic, many people do not want to go to the hospital, which affects their early diagnosis and later recovery. In addition, the steady growth of the elderly population is putting increasing pressure on geriatric care facilities around the world. The number of people who need fast, efficient care far exceeds the number of providers available to care for them. According to the World Health Organization, we are facing a global shortage of 7 million health workers. That's the bad news. The good news is that rapid advances in technologies like edge computing and 5G are making it easier to introduce solutions that can fill the workforce shortage and change the way health care is delivered. Let's look at some examples of medical applications for edge computing and 5G. Wearable Wearable devices can continuously monitor blood pressure, heart rate, body temperature, oxygen levels, and more. This data is pushed to the nearest edge server and processed locally at that edge location, thus minimizing latency and increasing processing speed. Doctors can use this information to assess the patient's health status in real time. In the hospital Radar-enabled bedside sensors monitor vital signs such as heart rate, respiratory rate and blood pressure, while alerting caregivers when normal limits are exceeded. The bedside sensors track the amount of time people spend sleeping. The data obtained from sleep patterns can detect early signs of disease. Until recently, hospitals had a centralized structure where data was stored in the cloud. Various smaller clinics and medical centers are connected to a central location to store and process data. Edge computing provides hospitals with the benefit of storing data at the nearest edge location and enabling it to be processed quickly. This has an additional security benefit because the data is stored locally and not transmitted over long distances to the cloud, which reduces the risk of someone hacking into the data in the middle of the process. Point-of-care diagnostics and telemedicine On-demand health care in the form of mobile point-of-care diagnostics brings health care to people in urban and rural areas. Along with vital signs, specific data for key diseases such as diabetes and cardiovascular disease are pushed to the nearest edge server. This data can be processed, analyzed and transmitted to a physician in a remote location within minutes. The rapid availability of health data has led to the growth of remote health applications, resulting in increased demand for capacity at service provider sites. Edge computing helps developers quickly add additional computing and storage capacity to meet urgent needs and optimize resources. Ambulances Ambulances can now do more than just transport patients to and from the hospital. Technology embedded in point-of-care screening devices and HD video can transmit data on vital signs and other health parameters to a central monitoring station via a 5G connection and back through the network of last-mile edge providers. Paramedics and emergency medical responders can then work with physicians to stabilize patients before they are transported to the hospital, while emergency room staff can prepare for the patient's arrival. Artificial Intelligence No discussion of the future of health care would be complete without artificial intelligence. From chronic disease and cancer to radiology and risk assessment, we can use AI to transform patient care and diagnosis. Here are two examples of how AI is transforming early detection and diagnosis. Melanoma detection - Melanoma is a malignant tumor that accounts for more than 70% of skin cancer-related deaths. Doctors often rely on visual inspection to identify suspicious skin lesions. Although in many cases it is difficult to make an accurate diagnosis. Artificial intelligence can help solve this problem. Software systems using DCNNs (Deep Convolutional Neural Networks) can analyze wide-angle images from smartphone cameras and identify lesions that need further investigation. By storing the images in the nearest edge server and processing them locally, the results are returned within minutes. Recent research by Facebook and New York University Grossman School of Medicine shows that fast MRI images generated by artificial intelligence contain diagnostic information comparable to images taken by slower conventional MRI scanners. By removing about three-quarters of the raw data used to create the scans, the AI model was able to generate fastMRI scans comparable to the fastMRI scans created by normal MRI procedures. Because fastMRI scans require four times less data, they have the potential to scan patients faster, thus reducing their time on the MRI machine. Edge and 5G go hand in hand It's a mistake to think of 5G or edge metering in isolation. Edge metering is the only way to achieve the 5G goal of less than 5 ms latency. While most people think of 5G as having lower latency, they forget about the amount of data generated by edge devices. Devices such as wearables, sensors and other Internet of Things devices generate a lot of data that needs to be managed and processed locally, and the results transmitted back to doctors, hospital emergency rooms and remote facilities in near real-time. However, this is not always possible due to the less-than-ideal routing infrastructure of many telecommunications companies. 5G with edge promises to solve this problem by significantly reducing latency between endpoints and data centers. Translated with www.DeepL.com/Translator (free version)