Artificial Intelligence and Big Data in Public Health

被引：170
作者：
Benke, Kurt [1 ,2 ]
Benke, Geza [3 ]
机构：
[1] Univ Melbourne, Sch Engn, Parkville, Vic 3010, Australia
[2] State Govt Victoria, Ctr AgriBiosci, AgriBio, Bundoora, Vic 3083, Australia
[3] Monash Univ, Sch Publ Hlth & Prevent Med, 553 St Kilda Rd, Melbourne, Vic 3004, Australia
来源：
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH | 2018年 / 15卷 / 12期
关键词：
algorithms; Big Data; machine learning; deep learning; data mining; visualization; epidemiology; predictive analytics; precision medicine; vision; wearable AI; DIABETIC-RETINOPATHY; SURVEILLANCE TOOLS; PRECISION MEDICINE; SOCIAL MEDIA; EYE;
D O I：
10.3390/ijerph15122796
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Artificial intelligence and automation are topics dominating global discussions on the future of professional employment, societal change, and economic performance. In this paper, we describe fundamental concepts underlying AI and Big Data and their significance to public health. We highlight issues involved and describe the potential impacts and challenges to medical professionals and diagnosticians. The possible benefits of advanced data analytics and machine learning are described in the context of recently reported research. Problems are identified and discussed with respect to ethical issues and the future roles of professionals and specialists in the age of artificial intelligence.
引用
收藏
页数：9
相关论文
共 37 条
[11]  
Dean J., 2012, P ADV NEUR INF PROC, V1, P1223
[12]   Surveillance Tools Emerging From Search Engines and Social Media Data for Determining Eye Disease Patterns [J].
Deiner, Michael S. ;
Lietman, Thomas M. ;
McLeod, Stephen D. ;
Chodosh, James ;
Porco, Travis C. .
JAMA OPHTHALMOLOGY, 2016, 134 (09) :1024-1030
[13]  
Greget M., NUEYES
[14]   Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs [J].
Gulshan, Varun ;
Peng, Lily ;
Coram, Marc ;
Stumpe, Martin C. ;
Wu, Derek ;
Narayanaswamy, Arunachalam ;
Venugopalan, Subhashini ;
Widner, Kasumi ;
Madams, Tom ;
Cuadros, Jorge ;
Kim, Ramasamy ;
Raman, Rajiv ;
Nelson, Philip C. ;
Mega, Jessica L. ;
Webster, R. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2016, 316 (22) :2402-2410
[15]  
Han J, 2012, MOR KAUF D, P1
[16]   Real-world data is dirty: Data cleansing and the merge/purge problem [J].
Hernandez, MA ;
Stolfo, SJ .
DATA MINING AND KNOWLEDGE DISCOVERY, 1998, 2 (01) :9-37
[17]   Mining electronic health records: towards better research applications and clinical care [J].
Jensen, Peter B. ;
Jensen, Lars J. ;
Brunak, Soren .
NATURE REVIEWS GENETICS, 2012, 13 (06) :395-405
[18]   Adapting to Artificial Intelligence Radiologists and Pathologists as Information Specialists [J].
Jha, Saurabh ;
Topol, Eric J. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2016, 316 (22) :2353-2354
[19]   Visualization for genomics: The microbial genome viewer [J].
Kerkhoven, R ;
van Enckevort, FHJ ;
Boekhorst, J ;
Molenaar, D ;
Siezen, RJ .
BIOINFORMATICS, 2004, 20 (11) :1812-1814
[20]   Confounding by Indication in Clinical Research [J].
Kyriacou, Demetrios N. ;
Lewis, Roger J. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2016, 316 (17) :1818-1819
1234