PMID- 32416782
OWN - NLM
STAT- MEDLINE
DCOM- 20200527
LR  - 20210110
IS  - 1474-547X (Electronic)
IS  - 0140-6736 (Print)
IS  - 0140-6736 (Linking)
VI  - 395
IP  - 10236
DP  - 2020 May 16
TI  - Artificial intelligence and the future of global health.
PG  - 1579-1586
LID - S0140-6736(20)30226-9 [pii]
LID - 10.1016/S0140-6736(20)30226-9 [doi]
AB  - Concurrent advances in information technology infrastructure and mobile computing 
      power in many low and middle-income countries (LMICs) have raised hopes that 
      artificial intelligence (AI) might help to address challenges unique to the field of 
      global health and accelerate achievement of the health-related sustainable 
      development goals. A series of fundamental questions have been raised about 
      AI-driven health interventions, and whether the tools, methods, and protections 
      traditionally used to make ethical and evidence-based decisions about new 
      technologies can be applied to AI. Deployment of AI has already begun for a broad 
      range of health issues common to LMICs, with interventions focused primarily on 
      communicable diseases, including tuberculosis and malaria. Types of AI vary, but 
      most use some form of machine learning or signal processing. Several types of 
      machine learning methods are frequently used together, as is machine learning with 
      other approaches, most often signal processing. AI-driven health interventions fit 
      into four categories relevant to global health researchers: (1) diagnosis, (2) 
      patient morbidity or mortality risk assessment, (3) disease outbreak prediction and 
      surveillance, and (4) health policy and planning. However, much of the AI-driven 
      intervention research in global health does not describe ethical, regulatory, or 
      practical considerations required for widespread use or deployment at scale. Despite 
      the field remaining nascent, AI-driven health interventions could lead to improved 
      health outcomes in LMICs. Although some challenges of developing and deploying these 
      interventions might not be unique to these settings, the global health community 
      will need to work quickly to establish guidelines for development, testing, and use, 
      and develop a user-driven research agenda to facilitate equitable and ethical use.
CI  - Copyright © 2020 Elsevier Ltd. All rights reserved.
FAU - Schwalbe, Nina
AU  - Schwalbe N
AD  - Heilbrunn Department of Population and Family Health, Columbia Mailman School of 
      Public Health, New York, NY, USA; Spark Street Advisors, New York, NY, USA. 
      Electronic address: nschwalbe@ssc.nyc.
FAU - Wahl, Brian
AU  - Wahl B
AD  - Spark Street Advisors, New York, NY, USA; Department of International Health, Johns 
      Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
LA  - eng
PT  - Journal Article
PT  - Review
TA  - Lancet
JT  - Lancet (London, England)
JID - 2985213R
SB  - AIM
SB  - IM
MH  - *Artificial Intelligence
MH  - Disease Outbreaks/prevention & control
MH  - Global Health/*trends
MH  - Health Policy
MH  - Humans
MH  - Risk Assessment
PMC - PMC7255280
EDAT- 2020/05/18 06:00
MHDA- 2020/05/28 06:00
CRDT- 2020/05/18 06:00
PHST- 2019/07/21 00:00 [received]
PHST- 2020/01/21 00:00 [revised]
PHST- 2020/01/22 00:00 [accepted]
PHST- 2020/05/18 06:00 [entrez]
PHST- 2020/05/18 06:00 [pubmed]
PHST- 2020/05/28 06:00 [medline]
AID - S0140-6736(20)30226-9 [pii]
AID - 10.1016/S0140-6736(20)30226-9 [doi]
PST - ppublish
SO  - Lancet. 2020 May 16;395(10236):1579-1586. doi: 10.1016/S0140-6736(20)30226-9.