China Leads the Way in Adopting AI in Health Tech
A live video of a 76-year-old woman talking about her cooking plays on Li Hong’s phone. Li is in London, 8,700 km from his mother in the Chinese city of Kunming.
Li reduced the distance between them by installing cameras in her mother’s apartment, where she lives alone. The system has built-in microphones and speakers, allowing the pair to discuss the latest blood pressure readings from Li’s mother, who has a heart condition. “It’s like I’m back in China with her. The technology is so convenient,” Li says.
China has been quick to deploy a range of new technologies to ease the burden on hospitals, care systems and families caring for the sick and elderly. But it’s in medical artificial intelligence that the country’s early adoption of new solutions has been particularly notable, says Eric Topol, an American physician and author of Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again.
China has moved faster than the United States in medical AI from research to implementation, in part due to the availability of high-quality data, Topol says. “China has a huge data advantage when it comes to medical AI research,” he says, explaining that Chinese researchers can train AI models on datasets spanning entire provinces. In contrast, their US counterparts are limited to working with information from single hospitals – largely operated by private companies that keep records on internal servers.
AI is widely used in healthcare to help doctors analyze scans and images, improving the speed and accuracy of their diagnoses. Airdoc, a Beijing-based medical AI group, recently became the first company to win regulatory approval for its retina scanning software to be deployed in Chinese hospitals. “The eye is a window to the rest of the body,” says He Chao, Airdoc’s chief technology officer, noting that changes in the retina, including discoloration, can provide clues to conditions such as high blood pressure and diabetes.
“In China, some of the early adoption of medical AI is also driven by need,” says Topol. “They don’t have enough radiologists and doctors to match the population.” Airdoc’s retina scanners have been deployed to rural hospitals that lack specialist eye doctors – China has 44,800 such practitioners to serve its aging population of 1.4 billion.
The success of companies such as Airdoc is based on their access to large amounts of diverse medical data from Chinese patients. This wealth of information allows researchers to train algorithms that will eventually perform functions in clinical settings, such as diagnosing diseases from medical images and scans.
In the case of nearsightedness (myopia) – which affects 53% of children and adolescents in China – Airdoc has developed a machine learning model that measures the size and shape of the lens in a patient’s eye. Implantable collameric lens (ICL) surgery is an increasingly common procedure, in which an artificial lens is implanted between the natural lens of an eye and the iris to produce clearer vision.
However, the process is very complex due to possible postoperative pupil and iris changes which could mean that the lens does not fit properly. An article in the British Journal of Ophthalmology describes how Airdoc’s machine learning model provides over 80% accuracy in predicting these changes and selecting the correct ICL size.
“Hospitals are motivated to pursue this digital transformation as China faces a tough healthcare challenge,” said Sally Ye, a Shanghai-based healthcare analyst at Omdia, a technology consultancy. “The medical infrastructure is insufficient and digitizing AI is a way to solve this problem.”
Ye says Chinese AI companies have an advantage over those elsewhere because China has an abundance of low-cost labor needed to annotate medical data and standardize it for machine learning.
“China has a large workforce of data scientists, computer engineers and medical professionals who can work on these labor-intensive projects at relatively low cost” , she says. Policymakers in Beijing have thrown their support behind medical AI companies that come up with technological innovations to ease the burden on the country’s hospital system. Medical and health technologies are a central pillar of the “Healthy China 2030” flagship policy.
The money flowed into medical AI after the policy was released in 2016, with major internet companies and start-ups battling to be the first to gain regulatory approval and be deployed in Chinese hospitals.
In 2020 alone, Chinese start-ups attracted $1.4 billion in funding, compared to $2.4 billion for their US counterparts, and the two countries accounted for 90% of global start-up investment of medical AI, according to research by Omdia.
But the race ahead has also encouraged some companies to obtain data through unregulated channels. CN-Healthcare, a Chinese medical media platform and consulting firm, reported that in 2017 third-party data brokers were selling hospital medical records to AI companies.
“Medical AI companies lack a strong understanding of data protection,” says Deng Yong, associate professor of medical and health law at Beijing Chinese Medicine University, adding that they have had tend to see data compliance as a barrier.
Ensuring medical data is both anonymized and secure is expensive, and Deng says there has been a tendency to take shortcuts. Last year, a group of Chinese researchers discovered technical vulnerabilities in the way hospitals on the mainland handled patient data, which revealed the identities of individuals during a data breach.
Hackers have also been on the lookout for poorly secured medical records or data from wearable health devices, which can be sold to other medical companies or criminals, who use the information for blackmail or to make false medical claims.
In 2020, Cyble, an American cybersecurity group, identified a data breach on Chinese artificial intelligence health company Huiying, a manufacturer of medical imaging devices.
Beenu Arora, founder and chief executive of Cyble, claims that personal health records and Covid-19 test results were extracted from the company’s servers and put up for sale on the dark web. Huiying did not respond to a request for comment.
Arora says the digitalization of healthcare, which has accelerated during the pandemic, has increased the intensity of cyberattacks against the medical, healthcare and pharmaceutical industries. “These violations can lead to the use of patient histories for potential abuse or criminal activity,” he says.
The vulnerabilities found in Huiying’s database are not unique to China. Tech blog The Verge reported in early December that, based on government data, the personal health information of more than 40 million people in the United States was exposed in data breaches in 2021.
But, while health organizations in the United States must report health and medical data breaches when they affect 500 or more people, the same requirement does not exist in China. Nevertheless, a partner at a law firm in Shanghai says that although data breaches have occurred in China, none have been “very serious”, adding that “the general trend in China is towards place of a better privacy protection regime”.
At Airdoc, He Chao says the company has the “strictest procedures” and has invested in data protection by both anonymizing the medical records on which its algorithms are trained and inviting external cybersecurity firms to test. vulnerabilities. “These costs are a necessity,” says Chao. “Our business is built on data.”
These security measures are becoming an industry standard after Beijing introduced the Personal Information Protection Law in November, designed to prevent data hacking and other harmful uses of sensitive personal information.
Much like the EU’s General Data Protection Regulation, the PIPL stipulates that an individual’s explicit consent must be obtained before their medical data is collected and places the onus on medical AI companies to ensure the data security.
China’s AI community is debating how best to secure data privacy, says Jeffrey Ding, postdoctoral fellow at Stanford University’s Center for International Security and Cooperation in the United States and author of the ChinAI newsletter .
“Federated learning is becoming increasingly popular in China as a privacy tool,” Ding says, referring to the practice of streaming datasets across multiple servers to enhance security. “You can never guarantee privacy,” Topol says, “but AI helps us improve on that.”