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Artificial Intelligence-Powered Chinese Robot Passes Medical Licensing Exam
Artificial Intelligence-Powered Chinese Robot Passes Medical Licensing Exam

Artificial Intelligence-Powered Chinese Robot Passes Medical Licensing Exam

Published Updated

Key takeaways

  • Xiaoyi, an AI-powered robot, became the first to pass China's medical licensing exam.
  • Developed by iFlyTek and Tsinghua University, Xiaoyi completed the exam significantly faster than the allowed time.
  • The robot achieved a score well above the passing mark, demonstrating its mastery of medical knowledge.
  • Experts noted that Xiaoyi not only excelled academically but also showed empathy in patient treatment scenarios.
  • The achievement positions Xiaoyi ahead of other AI systems like IBM's Watson in medical qualification.
5 min
Xiaoyi robot Chinese technology company iFlyTek achieved a major breakthrough when one of its intelligent robots Xiaoyi recently cleared China’s national exam and qualified as a doctor. Xiaoyi, which means 'Little Doctor', became the first Artificial Intelligence-powered to pass the exam, taking a fraction of the allowed time to complete the test, said iFlyTek representatives, who developed the machine with Tsinghua University. "We have leapfrogged IBM’s Watson in becoming the first AI [robot] to qualify as a human doctor. Watson hasn’t passed such a licensing exam in the United States,” said Liu Qingfeng, the chairman of iFlyTek. Not only did the robot pass the exam, it actually got a score of 456 points, which is 96 points above the required marks. "This shows that it has indeed mastered the medical knowledge and clinical knowledge, and it has owned the basic ability to employ the knowledge to solve some problems," he added. Wu Ji, deputy director of Tsinghua’s electrical engineering department, said that Xiaoyi was more than able to display better knowledge than humans. But he also added that Xiaoyi was empathetic towards while treating the sick. "Quite a few questions are case analyses, that is, you are informed the basic conditions of some patients and some of their symptoms, then you have to tell what disease it is, what treatment should be adopted or what medicine they should take. Many are questions like this. Such complicated questions cannot be answered only by searching. Therefore, it is essentially a reasoning process based on knowledge and probability model."
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