Evidence map›Paper›PMID 35199066›Full record

ReviewPatterns (New York, N.Y.)2022

Optimizing human-centered AI for healthcare in the Global South.

Chinasa T Okolo

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Chinasa T OkoloDepartment of Computer Science, Cornell University, Ithaca, NY 14853, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past 60 years, artificial intelligence (AI) has made significant progress, but most of its benefits have failed to make a significant impact within the Global South. Current practices that have led to biased systems will prevent AI from being actualized unless significant efforts are made to change them. As technical advances in AI and an interest in solving new problems lead researchers and tech companies to develop AI applications that target the health of marginalized communities, it is crucially important to study how AI can be used to empower those on the front lines in the Global South and how these tools can be optimally designed for marginalized communities. This perspective examines the landscape of AI for healthcare in the Global South and the evaluations of such systems and provides tangible recommendations for AI practitioners and human-centered researchers to incorporate in the development of AI systems for use with marginalized populations.

Indexed as

artificial intelligencecommunity health workersethicsglobal healthhuman-centered AImachine learningMLparticipatory designtechnical evaluations

Identifiers

PMID35199066
PMCPMC8848006

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.