Exploring the perspective of emergency and critical care medicine
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Date
2025
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Addis Ababa Universtity
Abstract
The rapid global advancement of Artificial Intelligence (AI) presents a
transformative potential for healthcare The integration of Artificial Intelligence (AI) ,
particularly in high burden, time-sensitive disciplines such as Emergency and Critical Care
Medicine (ECCM) holds significant potential to enhance diagnostics, decision-making and
minimize workload burden However, its adoption in low-resource
settings remain uncertain, with limited understanding of the perspectives and
readiness of frontline practitioners. This study explores the perspective of ECCM
residents in Addis Ababa, Ethiopia regarding AI’s integration in clinical practice.
Objective: The objective of this study is to explore the perspectives of Emergency and
Critical Care Medicine (ECCM) residents regarding the integration of Artificial Intelligence
(AI) in their clinical practice at three major teaching hospitals in Addis Ababa, Ethiopia.
Methodology: A qualitative, phenomenological, multi-center study was conducted from June
1 to October 30, 2025, at three major teaching hospitals in Addis Ababa. Using purposive
sampling, sixteen ECCM residents participated. One focus group discussion with eight
members and eight in-depth interviews were conducted. Data were collected from ECCM
residents through audio recordings in the Amharic language, then transcribed and translated
by the principal investigator. After familiarization with the data, initial open coding was
generated, followed by axial coding. Five themes with three subthemes for each theme were
developed, each theme and subtheme were defined and supported by verbatim quotes.
thematic analysis was conducted manually.
Results: Five major themes emerged (1) a foundational Knowledge gap alongside
conceptual understanding of AI (2) strong belief in AI’s potential clinical Benefits for
decision support, diagnostic accuracy, and burnout mitigation (3) profound systemic
Barriers including financial constraints, lack of formal training, infrastructure limitations and
lack of data governance (4) Attitudes of cautious optimism coupled with ethical concerns
about accountability and skill erosion and (5) clear prerequisites of AI Implementation
requiring AI literacy training, national policy, and infrastructure investment.
Conclusion and Recommendations: ECCM residents' positive attitude towards AI as a
supportive tool rather than a replacement for clinical judgment and its perceived utility in
mitigating burnout whereas key barriers include a lack of formal training and practical AI
exposure, inadequate digital infrastructure, absence of regulatory frameworks, and fears
regarding clinical autonomy and liability. We recommend formal AI training for the health
professionals and conducting further research on perceptions of stakeholder at medical
curricula to develop national AI integration policies.
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Keywords
Artificial Intelligence, Emergency Medicine, Critical Care, Clinical Practice, Qualitative Research, perspective of residents, Implementation Barriers, Healthcare Technology, Ethiopia