Evidence map›Paper›PMID 37954052›Full record

ReviewFrontiers in public health2023

Using artificial intelligence to improve public health: a narrative review.

David B Olawade, Ojima J Wada, Aanuoluwapo Clement David-Olawade, Edward Kunonga, Olawale Abaire, Jonathan Ling

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
103citing papers in PubMed, 2 pooled it
48.6field-weighted citation impact, top 1% of its field
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

103 citing papers in PubMed, 2 syntheses or guidelines pooled it, 212 citations in OpenAlex.

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43 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

6 authors at 5 institutions in 3 countries.

David B OlawadeDepartment of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom.
Ojima J WadaDivision of Sustainable Development, Qatar Foundation, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Aanuoluwapo Clement David-OlawadeEndoscopy Unit, NHS Trust, Epsom and St. Helier University Hospitals, Carshalton, United Kingdom.
Edward KunongaSchool of Health and Life Sciences, Teesside University, Middlesbrough, United Kingdom.
Olawale AbaireDepartment of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Nigeria.
Jonathan LingIndependent Researcher, Stockton-on-Tees, United Kingdom.
Adekunle Ajasin University · NGEpsom and St Helier University Hospitals NHS Trust · GBHamad bin Khalifa University · QATeesside University · GBUniversity of East London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is a rapidly evolving tool revolutionizing many aspects of healthcare. AI has been predominantly employed in medicine and healthcare administration. However, in public health, the widespread employment of AI only began recently, with the advent of COVID-19. This review examines the advances of AI in public health and the potential challenges that lie ahead. Some of the ways AI has aided public health delivery are via spatial modeling, risk prediction, misinformation control, public health surveillance, disease forecasting, pandemic/epidemic modeling, and health diagnosis. However, the implementation of AI in public health is not universal due to factors including limited infrastructure, lack of technical understanding, data paucity, and ethical/privacy issues.

Indexed as

Artificial IntelligenceCOVID-19Delivery of Health CareHumansPandemicsPublic Healthartificial intelligence (AI)healthcarehealth policypublic healthreview

Identifiers

PMID37954052
PMCPMC10637620
OpenAlexW4387956898

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.