In Depth

How Tencent’s AI can diagnose Parkinson’s disease ‘within minutes’

New technology could help patients track their condition from their smartphones

The Chinese technology giant Tencent has developed an artificial intelligence (AI) programme that can speed up the process of diagnosing Parkinson’s disease. 

Developed in collaboration with the London-based medical firm Medopad, the AI uses cameras that monitor how patients move their hands in order to evaluate the severity of their symptoms.

The AI has been trained to identify symptoms by studying existing video footage of Parkinson’s patients, says Forbes. The process, often referred to as machine learning, was completed with the help of London’s King’s College Hospital.

The process of monitoring a patient’s symptoms normally takes about half an hour, but the tech developed through the partnership could cut that down to three minutes.

According to Wei Fan, Tencent’s head of medical research, the technology would allow medical professionals to set drug dosage levels and monitor patients without the need for hospital visits, Bloomberg reports. 

In an interview with the BBC, Medopad’s chief executive Dan Vahdat said the company had been hoping to create an AI programme for smartphones that was capable of detecting Parkinson’s symptoms but couldn’t find a Britain-based firm that could match “what Tencent offered as a partner.”

“Our ambition is to impact a billion patients around the world – and to be able to get to that kind of scale we need to work with partners that have international reach”, he said.

Can these AI techniques be used for other diseases?

If the technology developed for Parkinson’s patients proves a reliable method for tracking symptoms it’s certainly a possibility. 

The technology is still a prototype. But if the AI could be transferred onto a smartphone – an area that Medopad specialises in – it could be adapted for other diseases. 

Fan and Vahdat told Bloomberg that the technology could be applied to childhood brain cancers, for example. It could also be used to monitor the progression of scoliosis (a spinal condition) in younger patients.

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