Evidence map›Paper›PMID 30233828›Full record

ReviewBMJ global health2018

Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings?

Brian Wahl, Aline Cossy-Gantner, Stefan Germann, Nina R Schwalbe

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in BMJ global health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 255 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
255citing papers in PubMed, 9 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

255 citing papers in PubMed, 9 syntheses or guidelines pooled it.

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195 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Brian WahlSpark Street Consulting, New York City, New York, USA.ORCID 0000-0002-0037-7364
Aline Cossy-GantnerFondation Botnar, Basel, Switzerland.
Stefan GermannFondation Botnar, Basel, Switzerland.
Nina R SchwalbeSpark Street Consulting, New York City, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of artificial intelligence (AI) has evolved considerably in the last 60 years. While there are now many AI applications that have been deployed in high-income country contexts, use in resource-poor settings remains relatively nascent. With a few notable exceptions, there are limited examples of AI being used in such settings. However, there are signs that this is changing. Several high-profile meetings have been convened in recent years to discuss the development and deployment of AI applications to reduce poverty and deliver a broad range of critical public services. We provide a general overview of AI and how it can be used to improve health outcomes in resource-poor settings. We also describe some of the current ethical debates around patient safety and privacy. Despite current challenges, AI holds tremendous promise for transforming the provision of healthcare services in resource-poor settings. Many health system hurdles in such settings could be overcome with the use of AI and other complementary emerging technologies. Further research and investments in the development of AI tools tailored to resource-poor settings will accelerate realising of the full potential of AI for improving global health.

Indexed as

artificial intelligencelow- and middle-income countriesprimary health

Identifiers

PMID30233828
PMCPMC6135465

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.