Evidence map›Paper›PMID 41128758›Full record

ArticleEuropean radiology2026

A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.

Ahmed Marey, Ona Ambrozaite, Ahmed Afifi, Ritu Agarwal, Rama Chellappa, Sola Adeleke, Muhammad Umair

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PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Ahmed MareyDepartment of Radiology, Sheikh Khalifa Medical City, AbuDhabi, United Arab Emirates. ahmed.ahmed1797@alexmed.edu.eg.ORCID http://orcid.org/0000-0002-3659-1696
Ona AmbrozaiteDepartment of Chemistry at the Krieger School of Arts & Sciences, Johns Hopkins University, Baltimore, MD, USA.
Ahmed AfifiBenha University Faculty of Medicine, Banha, Egypt.
Ritu AgarwalJohns Hopkins Carey Business School, Baltimore, MD, USA.
Rama ChellappaDepartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, USA.
Sola AdelekeSchool of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Muhammad UmairRussell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesArtificial intelligence (AI) promises to accelerate and democratize medical imaging, yet low- and middle-income countries (LMICs) face distinct barriers to adoption. This perspective identifies those barriers and proposes an action-oriented roadmap. MATERIALS AND

methodsInsights were synthesized from a Johns Hopkins Science Diplomacy Hub workshop (18 experts in radiology, AI, and health policy) and a scoping review of peer-reviewed and grey literature. Workshop discussions were transcribed, thematically coded, and iteratively validated to reach consensus.

resultsFive interlocking barriers were prioritized: (1) infrastructure gaps-scarce imaging devices, unstable power, and limited bandwidth; (2) data deficiencies-small, non-representative, or ethically constrained datasets; (3) workforce shortages and brain drain; (4) uncertain ethical, regulatory, and medicolegal frameworks; and (5) financing and sustainability constraints. Case studies from Nigeria, Uganda, and Colombia showed that low-field MRI, cloud-based PACS, community-engaged data collection, and public-private partnerships can successfully mitigate several of these challenges.

conclusionsTargeted policy levers-including shared procurement of low-cost hardware, regional AI and data hubs, train-the-trainer workforce programs, and harmonized regulation-can enable LMIC health systems to deploy AI imaging responsibly, shorten diagnostic delays, and improve patient outcomes. Lessons are transferable to resource-constrained settings worldwide. KEY POINTS: Question How can LMICs overcome infrastructure, data, workforce, regulatory, and financing barriers to implement artificial-intelligence tools in clinical medical imaging? Findings Our multinational consensus identifies five obstacles and maps each to actionable levers: low-cost hardware, regional data hubs, train-the-trainer schemes, harmonized regulation, blended financing. Clinical relevance Implementing these targeted measures enables LMIC health systems to deploy AI imaging reliably, shorten diagnostic delays, and improve patient outcomes while reducing dependence on external expertise.

Indexed as

Artificial IntelligenceDeveloping CountriesDiagnostic ImagingHealth PolicyHumansArtificial intelligenceHealth policyLow- and middle-income countries (LMICs)Medical imagingRadiology implementation

Identifiers

PMID41128758
PMCPMC13035529

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.