Artificial Intelligence in Global Health

被引:7
作者
Davies, Sara E. [1 ,2 ,3 ]
机构
[1] Griffith Univ, Sch Govt & Int Relat, Griffith, NSW, Australia
[2] Monash Univ, Monash Gender Peace & Secur Ctr, Sch Social Sci, Clayton, Vic, Australia
[3] Queensland Univ Technol, Fac Law, Brisbane, Qld, Australia
关键词
algorithm; gender; human rights; influenza; intersectionality; minority groups; pandemic; social media; surveillance; artificial intelligence; DISEASE;
D O I
10.1017/S0892679419000157
中图分类号
B82 [伦理学(道德学)];
学科分类号
摘要
Artificial intelligence (AI) is reaching into every aspect of global health. In this essay, I examine one example of AI's potential contributions and limitations in global health: the prediction, treatment, and containment of a global influenza outbreak. The potential advantages are clear. AI can aid global influenza surveillance platforms by improving the capacity of organizations to look for novel influenza outbreak strains in the right places, to identify populations most likely to spread influenza, and to produce real-time information about the disease's spread by monitoring social media communications to track outbreak events. There are also very real limitations to what AI can do, and it is crucial that AI not be used as an excuse not to invest in strengthening health systems and other traditional components of global healthcare. AI may also be able to improve our understanding of who should receive a vaccine and what is most effective for large-scale vaccine delivery, but there will always be blind spots that the data cannot fill. Investment in healthcare, with attention to the danger of minimal access to care for minority groups that are at risk and in fragile situations, remains the best chance to prepare communities for outbreak detection, surveillance, and containment.
引用
收藏
页码:181 / 192
页数:12
相关论文
共 37 条
[1]  
[Anonymous], 2018, TERMS REF ITU T FOC
[2]  
[Anonymous], 2017, BIG DAT ART INT ACH, Vvi
[3]  
[Anonymous], ARTIFICIAL INTELLIGE
[4]  
[Anonymous], 2015, EC HLTH BEN TOB TAX, P3
[5]   Progress and Remaining Gaps in Estimating the Global Disease Burden of Influenza [J].
Bresee, Joseph ;
Fitzner, Julia ;
Campbell, Harry ;
Cohen, Cheryl ;
Cozza, Vanessa ;
Jara, Jorge ;
Krishnan, Anand ;
Lee, Vernon .
EMERGING INFECTIOUS DISEASES, 2018, 24 (07) :1173-1177
[6]  
Centers for Disease Control and Prevention, 2018, HIST 1918 FLU PAND
[7]   Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations [J].
Chen, Jonathan H. ;
Asch, Steven M. .
NEW ENGLAND JOURNAL OF MEDICINE, 2017, 376 (26) :2507-2509
[8]   Forecasting demand for maternal influenza immunization in low- and lower-middle-income countries [J].
Debellut, Frederic ;
Hendrix, Nathaniel ;
Ortiz, Justin R. ;
Lambach, Philipp ;
Neuzil, Kathleen M. ;
Bhat, Niranjan ;
Pecenka, Clint .
PLOS ONE, 2018, 13 (06)
[9]  
Eccleston-Turner Mark, GLOBAL HLTH GOVERNAN, V12, P22
[10]  
Eccleston-Turner Mark, 2018, ANOTHER MAJOR FLU PA