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

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-doc...

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Detalles Bibliográficos
Autores principales: Wu, Ji, Liu, Xien, Zhang, Xiao, He, Zhiyang, Lv, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195515/
https://www.ncbi.nlm.nih.gov/pubmed/30341328
http://dx.doi.org/10.1038/s41467-018-06799-6
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author Wu, Ji
Liu, Xien
Zhang, Xiao
He, Zhiyang
Lv, Ping
author_facet Wu, Ji
Liu, Xien
Zhang, Xiao
He, Zhiyang
Lv, Ping
author_sort Wu, Ji
collection PubMed
description 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.
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spelling pubmed-61955152018-10-22 Master clinical medical knowledge at certificated-doctor-level with deep learning model Wu, Ji Liu, Xien Zhang, Xiao He, Zhiyang Lv, Ping Nat Commun Article 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. Nature Publishing Group UK 2018-10-19 /pmc/articles/PMC6195515/ /pubmed/30341328 http://dx.doi.org/10.1038/s41467-018-06799-6 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Wu, Ji
Liu, Xien
Zhang, Xiao
He, Zhiyang
Lv, Ping
Master clinical medical knowledge at certificated-doctor-level with deep learning model
title Master clinical medical knowledge at certificated-doctor-level with deep learning model
title_full Master clinical medical knowledge at certificated-doctor-level with deep learning model
title_fullStr Master clinical medical knowledge at certificated-doctor-level with deep learning model
title_full_unstemmed Master clinical medical knowledge at certificated-doctor-level with deep learning model
title_short Master clinical medical knowledge at certificated-doctor-level with deep learning model
title_sort master clinical medical knowledge at certificated-doctor-level with deep learning model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6195515/
https://www.ncbi.nlm.nih.gov/pubmed/30341328
http://dx.doi.org/10.1038/s41467-018-06799-6
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