Evidence map›Paper›PMID 42328230›Full record

ArticleAfrican journal of emergency medicine : Revue africaine de la medecine d'urgence2026

Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.

Ayalew Zewdie Tadesse, Biruktawit Lemma Zemede, Emnet Tesfaye Shimber, Sofia Kebede, Yohannes Fekele, Eric Nsengiyumva, Menbeu Sultan, Mohamed Farah Yusuf Mohamud, Winnie Mdundo, Laurent Appolinaire Manirafasha and 4 more

Abstract read
In one paragraph

Article in African journal of emergency medicine : Revue africaine de la medecine d'urgence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Beyond awareness: Defining AI readiness in Emergency Medicine across health systems.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026
    Article
  2. African emergency care systems: AI-aware but not AI-ready.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026
    Article
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

14 authors.

Ayalew Zewdie TadesseAfrica Health Sciences University, Kigali, Rwanda.
Biruktawit Lemma ZemedeAfrica Health Sciences University, Kigali, Rwanda.
Emnet Tesfaye ShimberAfrica Health Sciences University, Kigali, Rwanda.
Sofia KebedeAfrica Health Sciences University, Kigali, Rwanda.
Yohannes FekeleAfrica Health Sciences University, Kigali, Rwanda.
Eric NsengiyumvaAfrica Health Sciences University, Kigali, Rwanda.
Menbeu SultanAfrica Health Sciences University, Kigali, Rwanda.
Mohamed Farah Yusuf MohamudFaculty of Medicine and Surgery, Mogadishu, Somalia.
Winnie MdundoMuhimbili National Hospital-Mloganzila, Dar es Salaam, Tanzania.
Laurent Appolinaire ManirafashaAfrica Health Sciences University, Kigali, Rwanda.
Annick Blanche UmuhozaAfrica Health Sciences University, Kigali, Rwanda.
Shama PatelBrown University, Providence, RI, USA.
Mallika ManyapuGeorge Washington University, District of Columbia, Washington, USA.
Tsion FirewAfrica Health Sciences University, Kigali, Rwanda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Emergency Departments (EDs) in Africa face significant challenges including resource scarcity, overcrowding, and limited infrastructure. Artificial intelligence (AI) presents a promising opportunity to enhance emergency care delivery in these settings. Despite growing global interest, little is known about the perceptions, experiences, and readiness of African emergency medicine professionals regarding AI integration. This study evaluated the knowledge, perceived advantages, concerns and support requirements related to AI among emergency medicine professionals across sub-Saharan Africa. Methods: A cross-sectional mixed-method study was conducted among emergency medicine consultants and residents across 14 African countries. Data was collected via a self-administered online questionnaire adapted from a previously validated instrument and distributed through professional networks. Quantitative items captured demographic information, AI knowledge, usage, and perceptions, while open-ended qualitative questions explored experiences, expectations, and barriers. Descriptive statistics summarized quantitative data, and inductive thematic analysis was applied to qualitative responses. Cross tab and fisher exact analysis was done to assess association. Results: A total of 211 responses were analyzed (median age 32 years; 72.5 % male; 65.9 % consultants). Most respondents had a basic understanding of AI (88.2 %) and were aware of AI applications in emergency medicine (73.2 %), yet only 14.2 % had received formal training. While 73.0 % had used AI tools, with predominantly nonclinical use (research 31.8 % and medical writing 20.1 %) only 29.9 % reported routine clinical use. Only12.0 % indicated that their institution had a formal AI implementation strategy. Respondents expressed concerns regarding AI errors (99.1 %), ethical risks (93.8 %), job displacement (88.6 %), and high cost (85.3 %). The majority (64.5 %) identified training as the most critical support needed, followed by policy guidance (21.3 %). Overall, 78.0 % expected AI to be used in African EDs in the future, although many emphasized the importance of gradual, contextually appropriate integration with sustained human oversight. Conclusion: African emergency medicine professionals are aware of AI and recognize its potential benefits, but formal training, institutional strategies, and infrastructure remain limited. Optimizing AI adoption requires structured education, policy development, context-specific implementation strategies, and ethical safeguards. These findings provide actionable insights for the safe and effective integration of AI in resource-limited emergency care settings across Africa.

Indexed as

Artificial IntelligenceEducationEmergency medicineLower- and Middle-Income Countries (LMICs)Sub Saharan Africa

Identifiers

PMID42328230
PMCPMC13276553

What Socratic 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 Socratic graph.