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October 2, 2025Frontiers in Public HealthOpen Access

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

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Authors

OMOdhran MallonFLFreddy LippertEPEva Pilot

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Overview

Qualitative analysis reveals key factors impacting AI's effectiveness in prehospital care in LMICs, indicating the need for focused implementation strategies.

Key Points

  • AI deployment can improve prehospital emergency care in low- and middle-income countries, addressing health inequities.
  • High-quality and representative data are essential for effective AI models, as shown in the thematic analysis of emerging insights.
  • Resource gaps hinder AI implementation in LMICs, highlighting the need for targeted support and investment.
  • Human-centred design principles must be integrated for improved acceptance and utilization of AI technologies in emergency care.

Cite This Study

Mallon et al. (2025) studied this question.

synapsesocial.com/papers/68de68f683cbc991d0a21ea3https://doi.org/10.3389/fpubh.2025.1632029
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Systematic Literature Review of Artificial Intelligence in Prehospital Emergency Care2025
  2. 2Healthcare Providers’ Perceptions of Artificial Intelligence in Prehospital Care: A Qualitative Meta-Synthesis2025
  3. 3Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation2025
  4. 4Applications of Artificial Intelligence to Medical Infrastructure in Rural Asian Medical Centers2025
  5. 5Challenges and Opportunities of Artificial Intelligence Implementation in the Management of Out-of-Hospital Cardiac Arrest: Scoping Review2025