Artificial intelligence and the future of global health. Issue 10236 (16th May 2020)
- Record Type:
- Journal Article
- Title:
- Artificial intelligence and the future of global health. Issue 10236 (16th May 2020)
- Main Title:
- Artificial intelligence and the future of global health
- Authors:
- Schwalbe, Nina
Wahl, Brian - Abstract:
- Summary: 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-drivenSummary: 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. … (more)
- Is Part Of:
- Lancet. Volume 395:Issue 10236(2020)
- Journal:
- Lancet
- Issue:
- Volume 395:Issue 10236(2020)
- Issue Display:
- Volume 395, Issue 10236 (2020)
- Year:
- 2020
- Volume:
- 395
- Issue:
- 10236
- Issue Sort Value:
- 2020-0395-10236-0000
- Page Start:
- 1579
- Page End:
- 1586
- Publication Date:
- 2020-05-16
- Subjects:
- Medicine -- Periodicals
Medicine -- Periodicals
Medicine
Medicine
Electronic journals
Periodicals
610.5 - Journal URLs:
- http://www.thelancet.com/ ↗
http://www.sciencedirect.com/science/journal/01406736 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/S0140-6736(20)30226-9 ↗
- Languages:
- English
- ISSNs:
- 0140-6736
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5146.000000
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