research-article

AI in Global Health: The View from the Front Lines

Authors:

Neha KumarAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 598, Pages 1 - 21

https://doi.org/10.1145/3411764.3445130

Published: 07 May 2021 Publication History

Abstract

There has been growing interest in the application of AI for Social Good, motivated by scarce and unequal resources globally. We focus on the case of AI in frontline health, a Social Good domain that is increasingly a topic of significant attention. We offer a thematic discourse analysis of scientific and grey literature to identify prominent applications of AI in frontline health, motivations driving this work, stakeholders involved, and levels of engagement with the local context. We then uncover design considerations for these systems, drawing from data from three years of ethnographic fieldwork with women frontline health workers and women from marginalized communities in Delhi (India). Finally, we outline an agenda for AI systems that target Social Good, drawing from literature on HCI4D, post-development critique, and transnational feminist theory. Our paper thus offers a critical and ethnographic perspective to inform the design of AI systems that target social impact.

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Index Terms

AI in Global Health: The View from the Front Lines
1. Applied computing
  1. Life and medical sciences
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

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Tohoku University, Japan
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University of New South Wales, Australia
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The University of Tokyo, Japan
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University of Copenhagen, Denmark
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Microsoft Research, USA

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https://dl.acm.org/doi/10.1145/3701216
Miran SSiraj MKhan NRehman AHussain SAhmad IRajper S(2024)Harnessing AI in Physical Therapy ModalitiesGenerative AI Techniques for Sustainability in Healthcare Security10.4018/979-8-3693-6577-9.ch014(269-278)Online publication date: 22-Nov-2024
https://doi.org/10.4018/979-8-3693-6577-9.ch014
Krones FWalker B(2024)From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settingsPLOS Digital Health10.1371/journal.pdig.00004373:12(e0000437)Online publication date: 4-Dec-2024
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