Browsing by Author "Tamrat Kifle"

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    Exploring the perspective of emergency and critical care medicine
    (Addis Ababa Universtity, 2025) Tamrat Kifle; Merahi kefyalew; Demmelash Gezahegn
    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.