Artificial intelligence in prehospital emergency care systems in low- and middle-income countries: cure or curiosity? Insights from a qualitative study

Abstract

Introduction: The adoption of artificial intelligence (AI) in prehospital emergency medicine has predominantly been confined to high-income countries, leaving untapped potential in low- and middle-income countries (LMICs). AI holds promise to address challenges in out-of-hospital care within LMICs, thereby narrowing global health inequities. To achieve this, it is important to understand the success factors and challenges in implementing AI models in these settings. Methods: A scoping review of peer-reviewed studies and semi-structured expert interviews were conducted to identify key insights into AI deployment in LMIC prehospital care. Data collection occurred between June and October 2024. Using thematic analysis, qualitative data was systematically coded to extract common themes within the studies and interview transcripts. Themes were then summarised narratively and supplemented with illustrative quotations in table format. Results: From 16 articles and nine expert interview transcripts, five core themes emerged: (1) the rapid, iterative development of AI technologies; (2) the necessity of high-quality, representative, and unbiased data; (3) resource gaps impacting AI implementation; (4) the imperative of integrating human-centred design principles; and (5) the importance of cultural and contextual relevance for AI acceptance. Conclusion: Additional focus on these areas can help drive the sustainable utilisation and ensuing development of AI in these environments. Strengthening collaboration and education amongst stakeholders and focusing on local needs and user engagement will be critical to promoting future success. Moving forwards, research should emphasise the importance of evidence-based AI development and appropriate data utilisation to ensure equitable, impactful solutions for all users.

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