Master clinical medical knowledge at certificated-doctor-level with deep learning model

Ji Wu (), Xien Liu, Xiao Zhang, Zhiyang He and Ping Lv
Additional contact information
Ji Wu: Tsinghua University
Xien Liu: Tsinghua University
Xiao Zhang: Tsinghua University
Zhiyang He: iFlytek Co.Ltd
Ping Lv: iFlytek Research

Nature Communications, 2018, vol. 9, issue 1, 1-7

Abstract: Abstract Mastering of medical knowledge to human is a lengthy process that typically involves several years of school study and residency training. Recently, deep learning algorithms have shown potential in solving medical problems. Here we demonstrate mastering clinical medical knowledge at certificated-doctor-level via a deep learning framework Med3R, which utilizes a human-like learning and reasoning process. Med3R becomes the first AI system that has successfully passed the written test of National Medical Licensing Examination in China 2017 with 456 scores, surpassing 96.3% human examinees. Med3R is further applied for providing aided clinical diagnosis service based on real electronic medical records. Compared to human experts and competitive baselines, our system can provide more accurate and consistent clinical diagnosis results. Med3R provides a potential possibility to alleviate the severe shortage of qualified doctors in countries and small cities of China by providing computer-aided medical care and health services for patients.

Date: 2018
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DOI: 10.1038/s41467-018-06799-6

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