US and China should collaborate more to bring AI to healthcare
Posted: 6 August 2019 | Rachael Harper (European Pharmaceutical Review) | No comments yet
New article says that to take full advantage of deep-learning solutions in healthcare, the US and China should collaborate, not compete.
In a new commentary article, titled ‘It Takes a Planet’, Eric Topol, MD, of Scripps Research and Kai-Fu Li, PhD, CEO of the China-based tech investment firm Sinovation Ventures have argued for more collaboration between China and the US on artificial intelligence (AI) development.
This comes in the wake of the US government ordering the AI company iCarbonX in China to divest its majority ownership stake in the Massachusetts-based company PatientsLikeMe.
“Chinese academics and companies already have unfettered access to personal health data,” they write. “To compete in AI health, US companies will need access to clinical data on a similar scale. How will that be possible if the current isolationist policy continues?”
Topol and Li continue that big data has changed the landscape of medicine, with every individual representing vast amounts of medical information that no human can adequately process. This is occurring at a time when there are unacceptable levels of medical errors, inefficiency, waste, burnout and depression among clinicians, and high costs for medical care.
They also note that poor access to medical care among people living in rural areas increases inequities in healthcare.
“These problems mandate big thinking on how we can pool our resources to promote better health everywhere and for everyone,” they continue. “We have at our fingertips technology capable of analysing petabytes of data. The difference now is that it is potentially achievable by capitalising on the ability to analyse the data rather than capitulating to the challenge.
“Let us embrace this opportunity by working together collaboratively across the planet for the greater good of all.”
The commentary article was published in Nature Biotechnology.
Related topics
Artificial Intelligence, Big Data, Data Analysis, Industry Insight, Research & Development (R&D), Technology