The rise of artificial intelligence and the uncertain future for physicians

被引:116
作者
Krittanawong, C. [1 ]
机构
[1] Icahn Sch Med Mt Sinai, Dept Internal Med, St Luke & Mt Sinai West, New York, NY 10029 USA
关键词
Big data; Artificial intelligence; Precision medicine;
D O I
10.1016/j.ejim.2017.06.017
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Physicians in everyday clinical practice are under pressure to innovate faster than ever because of the rapid, exponential growth in healthcare data. "Big data" refers to extremely large data sets that cannot be analyzed or interpreted using traditional data processing methods. In fact, big data itself is meaningless, but processing it offers the promise of unlocking novel insights and accelerating breakthroughs in medicine-which in turn has the potential to transform current clinical practice. Physicians can analyze big data, but at present it requires a large amount of time and sophisticated analytic tools such as supercomputers. However, the rise of artificial intelligence (AI) in the era of big data could assist physicians in shortening processing times and improving the quality of patient care in clinical practice. This editorial provides a glimpse at the potential uses of AI technology in clinical practice and considers the possibility of AI replacing physicians, perhaps altogether. Physicians diagnose diseases based on personal medical histories, individual biomarkers, simple scores (e.g., CURB-65, MELD), and their physical examinations of individual patients. In contrast, AI can diagnose diseases based on a complex algorithm using hundreds of biomarkers, imaging results from millions of patients, aggregated published clinical research from PubMed, and thousands of physician's notes from electronic health records (EHRs). While AI could assist physicians in manyways, it is unlikely to replace physicians in the foreseeable future. Let us look at the emerging uses of AI in medicine. (c) 2017 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:E13 / E14
页数:2
相关论文
共 9 条
[1]  
[Anonymous], 2017, ARXIV170302442
[2]  
[Anonymous], RADIOLOGY
[3]  
Chu C, 2016, F1000RESEARCH, P5
[4]  
De Fauw Jeffrey, 2016, F1000Res, V5, P1573
[5]   Dermatologist-level classification of skin cancer with deep neural networks [J].
Esteva, Andre ;
Kuprel, Brett ;
Novoa, Roberto A. ;
Ko, Justin ;
Swetter, Susan M. ;
Blau, Helen M. ;
Thrun, Sebastian .
NATURE, 2017, 542 (7639) :115-+
[6]   Artificial Intelligence in Precision Cardiovascular Medicine [J].
Krittanawong, Chayakrit ;
Zhang, HongJu ;
Wang, Zhen ;
Aydar, Mehmet ;
Kitai, Takeshi .
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2017, 69 (21) :2657-2664
[7]   Deep Learning With Unsupervised Feature in Echocardiographic Imaging [J].
Krittanawong, Chayakrit ;
Tunhasiriwet, Anusith ;
Zhang, HongJu ;
Aydar, Mehmet ;
Kitai, Takeshi .
JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY, 2017, 69 (16) :2100-2101
[8]   Healthcare in the 21st century [J].
Krittanawong, Chayakrit .
EUROPEAN JOURNAL OF INTERNAL MEDICINE, 2017, 38 :E17-E17
[9]  
Seals K, 2017, J VASC INTERV RADIOL, V28, pS153, DOI 10.1016/j.jvir.2016.12.974