A Robot Just Passed the Chinese Medical Exam

An AI-powered robot in China reportedly passed the country's medical licensing examination with flying colors. The news begs the question, are AI-powered doctors closer than we thought?

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Xiaoyi, an AI-powered robot developed by Hefei City, China-based iFlyTek, recently became the first robot to pass China's medical licensing exam, according to a South China Morning Post report. The robot scored an impressive 456 on the exam, 96 points above the required marks, and it only took a fraction of the allotted exam time.

iFlyTek developed Xiaoyi, which means “little doctor," with Tsinghua University in Beijing. According to Chinese news reports, more than half of the exam questions are derived from patient cases, so the robot could not rely solely on memorization and searches. Instead, Xiaoyi had to be able to develop the capacity to reason. The researchers used the clinical and diagnostic knowledge of medical professionals to tweak the robot's algorithms.

After Xiaoyi passed the exam, iFlyTek, which specializes in speech intelligence and artificial intelligence, said it would expand its business into new areas, including education and medical care.

Still, the more advanced AI technology becomes, the more some people fear such progress. But in a conversation with MD+DI sister publication Design News earlier this year, Apple Co-Founder Steve Wozniak tried to put some of those concerns to rest.

"There's sort of a fear with artificial intelligence that machines could become so intelligent and versatile that they could totally replace a person so there wouldn't be other jobs to go to, but that is so far off it's an unrealistic fear at this stage," Wozniak said. "It would take decades and decades."

Even machines like IBM's Watson, for example, have been programmed in its approach to cognitive computing.

"For 200 years, we've had machines that can make clothing better than a human," Wozniak pointed out. "It seems like they are thinking better and faster than us, but we told them what to think about, what to work on, what to learn and the method to learn it by, and then it learned very well."

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