Huawei and Philips develop AI health project in China
October 24, 2017
Huawei and Philips have developed a cloud-based AI healthcare project in China that uses machine-learning technologies.
"We worked with Huawei to establish a cloud platform, enable IoT access and develop solutions,” said Ludwig Liang (pictured), population health management director for Philips in China. “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 into the market."
Due to a sustained increase in life expectancy and an increasing prevalence of diseases, there is soaring demand for healthcare providers to deliver high-quality medical care. China's remote areas are in need of improved ways to tackle the growing healthcare demands and deliver convenient and safe patient care options.
Liang pointed out that cloud AI technologies were 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 an MoU to develop cloud healthcare primarily for second-tier cities in China, which would provide high-quality cloud healthcare services to communities that lacked advanced healthcare systems. This has completed testing and could create a new digital healthcare future that will transform healthcare systems in China by helping doctors make accurate decisions from insights gleaned from vast amounts of information.
The offering integrates Philips' personal health care, disease diagnosis and treatment, management expertise and system platform with Huawei's IT infrastructure, IoT connectivity and cloud AI capabilities to increase efficiency and accuracy of diagnostics and treatments.
AI can ease doctors' burden by identifying patterns from large volumes of data. The cloud and machine learning can precisely determine how much a patient's disease such as cancer has advanced, which can help doctors decide on appropriate treatment. Applying cloud AI technology can have an effect in underserved communities by automating visual diagnoses, reducing errors and lowering costs.
With advances in active monitoring, early intervention, remote treatment and other personal healthcare management methods, patients can consult with medical professionals any time to understand their health status. With the life records application, users can track their health. The information can be shared with healthcare professionals to bring up and ensure the appropriate care.
Bio-sensing wearables are advancing to help people with chronic conditions monitor health. In the future, data from bio-sensing wearables and mobile technologies can be uploaded to the cloud healthcare system and machine learning will help detect change and support early diagnosis of diseases. Smartphones can use voice analysis technology to identify blood pressure, heart diseases or Alzheimer's.
Cloud AI technology can assist doctors by automating repetitive work and analysing vast amounts of data and medical images to provide doctors with clinically relevant and high-quality data in real time.
Cloud AI technology uses machine-learning to analyse huge datasets to spot disease markers or mutations, and the technology can observe abnormalities significantly quicker and more accurately than humans.
A thrombus or bleeding can cause a stroke, which doctors must 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. Cloud AI technology can overcome this by analysing abnormalities and giving doctors the data to make fast diagnosis.
Secondly, cloud AI technology can help doctors share information more efficiently by quickly downloading large amounts of data to research a disease.
"Doctors can study very old case data, enabling them to further understand the symptoms from different diseases and identify efficient treatment," Liang said.