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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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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. |
format | Online Article Text |
id | pubmed-6195515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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