Designing AI tools to advance health equity in resource-constrained low- and middle-income countries.

Anindya Pradipta Susanto, David Lyell, Bambang Widyantoro, Shlomo Berkovsky, Farah Magrabi

Abstract read

In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Anindya Pradipta SusantoCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.ORCID https://orcid.org/0000-0001-5155-6904

David LyellCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.ORCID https://orcid.org/0000-0002-2695-0368

Bambang WidyantoroDepartment of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Centre Harapan Kita Hospital, Jakarta, Indonesia.ORCID https://orcid.org/0000-0001-5325-4125

Shlomo BerkovskyCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.ORCID https://orcid.org/0000-0003-2638-4121

Farah MagrabiCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.ORCID https://orcid.org/0000-0002-8426-5588

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the resource-constrained settings, shortages of health workers and underdeveloped infrastructure hinder the delivery of equitable, high quality care. This perspective outlines strategic principles to support the design of AI tools that are genuinely beneficial in low-resource contexts: adopting problem-driven approaches, understanding the socio-technical context, selecting appropriate clinical tasks, and ensuring point-of-care accessibility, clinical comprehensibility, and actionable recommendations, ultimately improving clinicians' decision-making and promoting health equity in low- and middle-income countries.

Indexed as

artificial intelligenceclinical decision supportdigital healthglobal healthhealth equityresource-limited settings

Identifiers

PMID42152871

DOI10.1177/20552076261438662

PMCPMC13180181

What OpenQuestion holds

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LicenceCC BY

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Registered trials

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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.