Evidence map›Paper›PMID 41118361›Full record

ArticlePloS one2025

Multilingual voice-enabled informatics tools: Catalyst for equitable AI in HIV and HIV-comorbidity healthcare management.

Olugbenga Oluseun Oluwagbemi, Emebo Onyeka, Abdulwahab Jatto, Folakemi E Oluwagbemi, Serengul Smith, Olufemi Oyelami, Segun Fatumo

Abstract read
In one paragraph

Article in PloS one, 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

7 authors.

Olugbenga Oluseun OluwagbemiDepartment of Computer Science, Faculty of Science and Technology, Middlesex University, The Burroughs, Hendon, London, United Kingdom.ORCID https://orcid.org/0000-0001-9085-3370
Emebo OnyekaDepartment of Computer Science, Virginia Tech, Blacksburg, Virginia, United States of America.
Abdulwahab JattoDepartment of Computer Science, Federal University Lokoja, Lokoja, Nigeria.
Folakemi E OluwagbemiFaculty of Health, Social Care & Education, Middlesex University, The Burroughs, Hendon, London, United Kingdom.
Serengul SmithDepartment of Computer Science, Faculty of Science and Technology, Middlesex University, The Burroughs, Hendon, London, United Kingdom.
Olufemi OyelamiComputer Science Programme, Bowen University, Iwo, Nigeria.ORCID https://orcid.org/0000-0002-5643-7266
Segun FatumoDepartment of Medicine, Precision Healthcare University Research Institute, Queen Mary University of London Empire House, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human Immunodeficiency Virus (henceforth HIV) is a global health problem, presently with no known cure. Africa has one of the highest incidences of HIV. Nigeria, within the West African (WA) region, is one of the largest economies on the continent. However, the country continues to struggle with HIV, with approximately 2 million individuals currently infected and experiencing ongoing transmissions. Management of the disease has been difficult due to communication barriers between English-speaking medical practitioners and indigenous patients in rural and suburban regions of the country and bordering countries. In this paper, we used fuzzy logic and voice-enabled technology to create WAHMIDS (West African HIV and HIV-comorbidity Multilingual Indigenous Diagnostic Software) and WAHMIMA (West African HIV Multilingual Informatics Mobile Application), which are health apps designed to help diagnose HIV and manage related health issues in both rural and urban areas for people who speak different indigenous languages in West Africa. Additionally, illustrations of the application of this tool to HIV diagnosis, using existing HIV data, are demonstrated. We expect that these tools will assist English-speaking medical workers and inhabitants of West African communities in their efforts to control HIV transmissions. These informatics tools have the potential to help prescribe medications for HIV and HIV-comorbidity patients. We anticipate that these informatics tools will help address healthcare disparities and promote diversity, equality, and inclusion by reducing the gaps in healthcare delivery between different regions and facilitating the collection of diverse patient data, which is essential for developing and planning more inclusive and accurate healthcare strategies in the West African sub-region.

Indexed as

Artificial IntelligenceHIV InfectionsMultilingualismDelivery of Health CareHumansLanguageNigeria

Identifiers

PMID41118361
PMCPMC12539699

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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.