1. Accelerating Disease Detection
Sick or injured patients understandably want prompt, accurate diagnoses as soon as possible. They often depend on radiologists to help, since medical imaging technologies can see things not verifiable through external exams alone.
A primary benefit of edge computing in radiology is that it can shorten the time required to get results and subsequent diagnoses. For example, NVIDIA has an edge computing reference application that can learn from a hospital’s local data.
The company intended to offer it to check for heart failure, cancer and neurodegenerative diseases before expanding the service. The tool can accomplish up to one-half trillion operations per second when used for image processing.
2. Shortening Medical Imaging Timeframes and Associated Decision-Making
Edge computing can also cut the times required to get useful images of a patient’s body. Certain options take longer than others. Taking an X-ray is a relatively short process. However, a magnetic resonance imaging (MRI) scan is a much longer procedure. Some of them require patients to lie still for up to an hour, a dreadful task for someone scared or in pain. However, AI can reconstruct medical images, plus add more clarity to tiny details.
Akshay Chaudhari, an assistant professor at Stanford University, says, “Using AI for MRI reconstruction can have a multitude of practical benefits — patients can undergo much faster imaging procedures, the images have a lower likelihood of having artifacts due to patient motion, hospitals can cater to more patients with shorter wait times, and radiologists can still render accurate diagnoses for their patients.”
GE Healthcare has an edge computing solution that aids with critical data collection and analysis, allowing medical experts to act sooner once the details about a patient are in hand. A stroke kills almost 2 million brain cells every minute, emphasizing why efficient decision-making is vital.
3. Enhancing Care for COVID-19 Patients
The COVID-19 pandemic forced healthcare facilities around the world to adapt to increased pressure and limited resources. The life-threatening risks faced by patients who contracted the virus made it even more important for medical professionals to diagnose them accurately and efficiently.
An edge computing solution used AI to help radiologists diagnose COVID-19 and pneumonia by picking up on the details they may miss due to fatigue and high workloads. It reportedly provided a 94% accuracy rate for those two ailments.
The pandemic highlighted the potential of other advanced technologies, too. For example, hospitals relied on asset tracking solutions to monitor resource usage, including equipment and bed availability. Smart sensors let physicians evaluate a patient’s condition during recovery, too.
4. Reducing Radiation Exposure
Changes in healthcare billing codes and policies often accompany medical innovations. For example, Medicare provides direct reimbursement for only two uses of AI in radiology. However, administrators at medical imaging facilities assess more than those payments. They also want evidence that an AI application gives clear benefits to patients and medical professionals.
One reason why edge computing and AI make such a beneficial pairing is that they can minimize a patient’s radiation exposure without sacrificing image clarity. More specifically, an option called compressed sensing generates high-quality images with less data.
That achievement translates into a safer experience. Using more radiation is an accepted way to make an image clearer. However, this example shows how AI could provide meaningful benefits in ways people don’t initially expect.
5. Alleviating Medical Imaging Specialist Shortages
Many healthcare systems do not have enough radiologists to meet patient needs. For example, a 2021 study from the United Kingdom found that 58% of radiology leaders lacked the diagnostic and interventional specialists needed to ensure patient safety.
Relatedly, the research highlighted a shortage of 1,939 medical imaging technicians required to get the healthcare infrastructure to meet pre-COVID-19 demand levels.
Edge computing won’t replace human expertise. However, when used strategically, it can relieve many of the burdens felt by overworked professionals trying to do their best.
Suppose an AI edge computing tool performs a preliminary analysis of a person’s scan and gives associated feedback. Then, it could reduce the overall time a radiologist looks at the results without sacrificing patient care. If that happens, people should find they can better cope with high workloads, even before staffing levels increase.
6. Minimizing Machine Outages and Assessing Overall Usage
Equipment failure in a radiology department can become a significant issue, sometimes forcing hospital administrators to direct incoming patients to other locations. However, work is underway in some medical facilities to apply edge computing for better visibility, which could include preventive maintenance.
Travis Shanahan, a systems architect at BevelCloud, helped install the necessary infrastructure at a California pediatric hospital. He explained, “There’s about a million health care machines in children’s hospitals. We think that if we connect them into the edge cloud securely and privately and maintain everything in the edge, we could build some interesting applications there that can’t exist in today’s healthcare systems.”
He continued, “They can monitor to prevent equipment failure. These hospitals are purchasing equipment all the time and often have no idea how often they are utilized or where they are in the hospital.”
When hospital decision-makers have a clearer idea of which medical imaging machines get used most often, they can make more informed plans for upkeep while simultaneously justifying new equipment investments.
Edge Computing Takes Radiology to New Levels
These six examples emphasize why bringing edge computing applications to medical imaging will push radiology forward, benefiting patients and technicians alike. Medicine is a fast-paced sector, and the professionals working in it become accustomed to that reality. However, as some of the uses here showed, edge computing can bring even more efficiency, plus enable better accuracy and reduce issues that compromise workflow.
Using edge computing in radiology is still in the relatively early stages. However, other benefits should become apparent as more facilities and medical professionals adopt it.