Huawei and Philips Unveil Jointly Developed Cloud AI Healthcare Solution in China

Huawei and Philips have successfully developed an advanced cloud healthcare solution in China. By using cloud and machine-learning technologies, the companies expect their jointly developed solution “to sweep across the healthcare industry at an unprecedented pace and scope, creating a new digital healthcare future.”

“We worked with Huawei to establish a cloud platform, enable IoT access, and develop solutions,” said Ludwig Liang, Population Health Management Director, Philips Greater China Region. “We have already tested some solutions on the cloud platform provided by Huawei and obtained very satisfying results. In the future, Philips and Huawei will work together to push healthcare solutions into the market.”

healthcare cloud hipaaChina’s remote areas are in need of improved ways to tackle the growing healthcare demands and deliver convenient and safe patient care options. Ludwig Liang points out that cloud AI technologies are especially important for China’s second-tier cities because many physicians “don’t necessarily have skills to read image diagnostics like MRI and CT scans. If a doctor processes thousands of images a day, he may miss something.”

In 2016, Huawei and Philips signed a MoU to develop a cloud healthcare solution primarily for second-tier cities in China, which would need to provide “high-quality” cloud healthcare services to communities that lacked advanced healthcare systems. The solution has now completed testing and is “set to create a new digital healthcare future, which would help transform healthcare systems in China by helping doctors make accurate decisions from insights gleaned from vast amounts of information.” 

Philips Personal Health Care

The Huawei-Philips Cloud Healthcare solution integrates Philips’ personal health care, disease diagnosis and treatment, management expertise and system platform with Huawei’s complete IT infrastructure, IoT connectivity and cloud AI capabilities to increase efficiency and accuracy of diagnostics and treatments.

Cloud AI would ease doctors’ burden by “easily” identifying patterns from large volumes of data. The cloud and machine-learning solution would be able to precisely determine how much a patient’s disease such as cancer has advanced, which can help doctors decide on appropriate treatment.

Another example. A thrombus or bleeding can cause a stroke, and doctors would need to diagnose and treat within the first 45 minutes to avoid life-altering or fatal outcomes, they must eliminate the thrombus as soon as symptoms appear. However, it can take hours and even days before identifying visible thrombus shadows through manual scanning. Using cloud AI technology can overcome by analyzing abnormalities and giving doctors the data to make fast diagnosis.

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