Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
Medical Business Department, iFlytek Co.Ltd, Hefei, 230088, China.
Nat Commun. 2018 Oct 19;9(1):4352. doi: 10.1038/s41467-018-06799-6.
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.
掌握医学知识对人类来说是一个漫长的过程，通常需要数年的学校学习和住院医师培训。最近，深度学习算法在解决医学问题方面显示出了潜力。在这里，我们通过一个深度学习框架 Med3R 展示了在认证医生水平上掌握临床医学知识的能力，该框架利用了类似人类的学习和推理过程。Med3R 成为第一个成功通过中国 2017 年国家医师资格考试笔试的人工智能系统，得分为 456 分，超过 96.3%的考生。Med3R 进一步应用于基于真实电子病历的辅助临床诊断服务。与人类专家和竞争基线相比，我们的系统可以提供更准确和一致的临床诊断结果。Med3R 通过为患者提供计算机辅助医疗和健康服务，为缓解中国欠发达地区和小城市合格医生的严重短缺问题提供了一种潜在的可能性。
