AI Diagnoses childhood diseases like doctors

AI

An artificial intelligence (AI) programme developed in China that combs through test results, health records and even handwritten notes diagnosed childhood diseases as accurately as doctors, researchers said.

From the flu and asthma to life-threatening pneumonia and meningitis, the system consistently matched or out-performed primary care pediatricians, they reported in Nature Medicine.

Dozens of studies in recent months have detailed how AI is revolutionizing the detection of diseases including cancers, genetic disorders and Alzheimer’s.

AI-based technology learns and improves in a way similar to humans, but has virtually unlimited capacity for data processing and storage.

“I believe that it will be able to perform most of the jobs a doctor does,” senior author Kang Zhang, a researcher at the University of California, San Diego, told AFP.

“But AI will never replace a doctor,” he added, comparing the relationship to an autonomous car that remains under the supervision of a human driver. It will simply allow doctors to do a better job in less time and at lower costs.”

The new technology, said Zhang, is the first in which AI absorbs unstructured data and “natural language” to imitate the process by which a physician figures out what’s wrong with a patient.

It can mimic a human pediatrician to interpret and integrate all types of medical data patient complaints, medical history, blood and imaging tests to make a diagnosis,” he said.

The system can be easily transferred to other languages and settings, he added.

By comparing hundreds of bits of information about a single patient with a vast store of acquired knowledge, the technology unearths links that previous statistical methods and sometimes flesh-and-blood doctors overlook.

To train the proof-of-concept system, Zhang and a team of 70 scientists injected more than 100 million data points from 1.3 million pediatrics patient visits at a major referral centre in Guangzhou, China.

The AI programme diagnosed respiratory infections and sinusitis a common sinus infection with 95 percent accuracy.

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