Evidence map›Paper›PMID 41422110›Full record

ArticleNature communications2025

Scoping review of artificial intelligence via mobile technology and social media for health in Africa.

Shakuntala Baichoo, Olubusola Oladeji, Leanne Villareal, Huguette Diakabana, Akinkunmi Paul Okekunle, Vukosi Marivate, Fred Kaggwa, Elaine O Nsoesie

Abstract readScoping Review
In one paragraph

Article in Nature communications, 2025. 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

8 authors.

Shakuntala BaichooDepartment of Digital Technologies, FoICDT, University of Mauritius, Moka, Mauritius. shakuntala.baichoo@uhn.ca.ORCID http://orcid.org/0000-0002-9335-1939
Olubusola Oladeji *Thunderbird School of Global Management, Arizona State University, Phoenix, AZ, USA.
Leanne Villareal *Center on the Ecology of Early Development, Boston University, Boston, MA, USA.
Huguette DiakabanaThe African Foundation for Peace and Security, Benmore, South Africa.
Akinkunmi Paul OkekunleCollege of Medicine, University of Ibadan, Ibadan, Nigeria.ORCID http://orcid.org/0000-0003-4825-4934
Vukosi MarivateDepartment of Computer Science, University of Pretoria, Pretoria, South Africa.
Fred KaggwaDepartment of Computer Science, Mbarara University of Science & Technology, Mbarara, Uganda.
Elaine O NsoesieData Science Africa, Nairobi, Kenya. onelaine@bu.edu.ORCID http://orcid.org/0000-0001-9170-8714

Funding

Wellcome Trust
6 · The paper itself

Abstract

The combination of mobile technologies and social media with Artificial Intelligence (AI) opens new opportunities for multi-modal data generation, analysis, and inference for various health applications. To investigate how these tools are being used for health applications in Africa, we conduct a scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. We screen 469 articles and synthesize 116. We include 29 studies documenting the use of a broad range of advanced and straightforward machine-learning techniques to study infectious and chronic diseases such as COVID-19 (4 studies, 13.8%), malaria (5, 17.2%), and cervical cancer (2, 6.9%). Countries with high internet and mobile phone penetration have higher representation. Based on identified gaps, we make research and policy recommendations to enhance the contribution of these tools in advancing health in Africa. These include investing in studies on chronic diseases and implementing frameworks to address geographic inequity.

Indexed as

Artificial IntelligenceCell PhoneSocial MediaAfricaCOVID-19HumansMachine LearningMalariaSARS-CoV-2

Identifiers

PMID41422110
PMCPMC12722415

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.