African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026
Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tadesse Ayalew Zewdie, Zemede Biruktawit Lemma, Shimber Emnet Tesfaye, Kebede Sofia, Fekele Yohannes, Nsengiyumva Eric, Sultan Menbeu, Mohamud Mohamed Farah Yusuf, Mdundo Winnie, Manirafasha Laurent Appolinaire, Umuhoza Annick Blanche, Patel Shama, Manyapu Mallika, Firew Tsion
Аннотация
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.
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.