Artificial Intelligence in Nephrology: How Can Artificial Intelligence Augment Nephrologists' Intelligence?

被引:29
作者
Xie, Guotong [1 ]
Chen, Tiange [1 ]
Li, Yingxue [1 ]
Chen, Tingyu [2 ]
Li, Xiang [1 ]
Liu, Zhihong [2 ]
机构
[1] Ping An Healthcare Technol, Beijing, Peoples R China
[2] Nanjing Univ, Sch Med, Jinling Hosp, Natl Clin Res Ctr Kidney Dis, Nanjing, Peoples R China
关键词
Kidney disease; Artificial intelligence; Big data; Diagnostics and prognostics; Treatment; OUTCOME PREDICTION; MACHINE; CANCER;
D O I
10.1159/000504600
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Background: Artificial intelligence (AI) now plays a critical role in almost every area of our daily lives and academic disciplines due to the growth of computing power, advances in methods and techniques, and the explosion of the amount of data; medicine is not an exception. Rather than replacing clinicians, AI is augmenting the intelligence of clinicians in diagnosis, prognosis, and treatment decisions. Summary: Kidney disease is a substantial medical and public health burden globally, with both acute kidney injury and chronic kidney disease bringing about high morbidity and mortality as well as a huge economic burden. Even though the existing research and applied works have made certain contributions to more accurate prediction and better understanding of histologic pathology, there is a lot more work to be done and problems to solve. Key Messages: AI applications of diagnostics and prognostics for high-prevalence and high-morbidity types of nephropathy in medical-resource-inadequate areas need special attention; high-volume and high-quality data need to be collected and prepared; a consensus on ethics and safety in the use of AI technologies needs to be built.
引用
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页码:1 / 6
页数:6
相关论文
共 28 条
[21]   Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning [J].
Na, Liangyuan ;
Yang, Cong ;
Lo, Chi-Cheng ;
Zhao, Fangyuan ;
Fukuoka, Yoshimi ;
Aswani, Anil .
JAMA NETWORK OPEN, 2018, 1 (08)
[22]   Machine Learning in Medicine [J].
Rajkomar, Alvin ;
Dean, Jeffrey ;
Kohane, Isaac .
NEW ENGLAND JOURNAL OF MEDICINE, 2019, 380 (14) :1347-1358
[23]   Big science and big data in nephrology [J].
Saez-Rodriguez, Julio ;
Rinschen, Markus M. ;
Floege, Juergen ;
Kramann, Rafael .
KIDNEY INTERNATIONAL, 2019, 95 (06) :1326-1337
[24]   Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning [J].
Shipp, MA ;
Ross, KN ;
Tamayo, P ;
Weng, AP ;
Kutok, JL ;
Aguiar, RCT ;
Gaasenbeek, M ;
Angelo, M ;
Reich, M ;
Pinkus, GS ;
Ray, TS ;
Koval, MA ;
Last, KW ;
Norton, A ;
Lister, TA ;
Mesirov, J ;
Neuberg, DS ;
Lander, ES ;
Aster, JC ;
Golub, TR .
NATURE MEDICINE, 2002, 8 (01) :68-74
[25]   Rural Health Care Access and Policy in Developing Countries [J].
Strasser, Roger ;
Kam, Sophia M. ;
Regalado, Sophie M. .
ANNUAL REVIEW OF PUBLIC HEALTH, VOL 37, 2016, 37 :395-412
[26]   A clinically applicable approach to continuous prediction of future acute kidney injury [J].
Tomasev, Nenad ;
Glorot, Xavier ;
Rae, Jack W. ;
Zielinski, Michal ;
Askham, Harry ;
Saraiva, Andre ;
Mottram, Anne ;
Meyer, Clemens ;
Ravuri, Suman ;
Protsyuk, Ivan ;
Connell, Alistair ;
Hughes, Cian O. ;
Karthikesalingam, Alan ;
Cornebise, Julien ;
Montgomery, Hugh ;
Rees, Geraint ;
Laing, Chris ;
Baker, Clifton R. ;
Peterson, Kelly ;
Reeves, Ruth ;
Hassabis, Demis ;
King, Dominic ;
Suleyman, Mustafa ;
Back, Trevor ;
Nielson, Christopher ;
Ledsam, Joseph R. ;
Mohamed, Shakir .
NATURE, 2019, 572 (7767) :116-+
[27]   Disease burden and challenges of chronic kidney disease in North and East Asia [J].
Wang, Jinwei ;
Zhang, Luxia ;
Tang, Sydney Chi-wai ;
Kashihara, Naoki ;
Kim, Yong-Soo ;
Togtokh, Ariunaa ;
Yang, Chih-wei ;
Zhao, Ming-hui .
KIDNEY INTERNATIONAL, 2018, 94 (01) :22-25
[28]   Acute Kidney Injury in Asia [J].
Yang, Li .
KIDNEY DISEASES, 2016, 2 (03) :95-102