Ethiopian Emergency Clinicians Show Strong AI Attitudes but Skill Gaps Persist

Healthcare professionals working in Ethiopian emergency departments report a high overall readiness to adopt artificial intelligence, even though most have never used such tools in practice, according to a cross-sectional survey published in BMC Health Services Research. The study, conducted between February 1 and March 22, 2026, measured readiness among 211 emergency department staff using an adapted version of the Medical Artificial Intelligence Readiness Scale, an instrument originally developed for medical students and reworded for clinicians working in high-pressure emergency settings. The headline finding is a striking mismatch between enthusiasm and experience.

Only 63 of the 211 participants, or 29.9 percent, reported having used artificial intelligence previously. Yet the group's mean readiness score was 4.12 on the study's scale, with a standard deviation of 0.85, indicating that positive disposition toward the technology runs well ahead of hands-on familiarity with it. Breaking the readiness score into its components reveals where clinicians feel strongest and weakest.

Ethical awareness was the highest-scoring domain, with a mean of 4.20 and a standard deviation of 0.75, followed by attitude toward artificial intelligence at a mean of 3.76. Knowledge and technical skills lagged behind at a moderate mean of 3.43, suggesting that clinicians recognize both the promise and the ethical stakes of algorithmic medicine but feel less confident operating the underlying tools. Participants also rated the obstacles standing in the way of adoption.

The perceived barriers composite scored a mean of 3.69 with a standard deviation of 0.62, and the most frequently cited problems were lack of training, limited digital infrastructure within healthcare institutions, and concerns about trusting artificial intelligence systems in clinical decision-making. To identify what actually drives readiness, the researchers used multivariable linear regression after preliminary bivariate analyses. Three factors emerged as significant predictors.

Vision, meaning participants' perception of a forward-looking institutional or professional direction for artificial intelligence, had the largest effect, with a regression coefficient of 0.673 and a p-value below 0.001. Ethical awareness contributed a smaller but significant effect, with a coefficient of 0.165 and a p-value of 0.006. The internal consistency of the adapted questionnaire was assessed using Cronbach's alpha, a standard statistical measure of how coherently a set of items measures a single construct.

The authors conclude that emergency department healthcare professionals in the surveyed Ethiopian institutions hold strong positive attitudes and ethical awareness toward artificial intelligence, but that moderate technical skills and significant structural barriers could impede successful integration. Because the study is cross-sectional, it captures a single moment in time and cannot establish causal relationships between the measured predictors and readiness; the findings also describe the surveyed institutions and may not generalize to all Ethiopian emergency departments or to other countries. Based on their results, the researchers recommend strengthening artificial intelligence-focused professional training programs, embedding artificial intelligence concepts into health professional education curricula, improving digital infrastructure within healthcare institutions, and establishing national regulatory and ethical frameworks to support the safe and effective adoption of artificial intelligence in emergency care settings.

The training gap appears particularly consequential given that knowledge and skills scored lowest among the readiness domains while fewer than a third of participants had ever used artificial intelligence tools.

Subject of Research: Artificial intelligence readiness among emergency department healthcare professionals in Ethiopia

Article Title: Artificial intelligence readiness among emergency department healthcare professionals: cross-sectional study from Ethiopia

Article References: Weldemedhn, S. G., Berhanu, M. T., Taye, B. T., Walte, Y. S., Mengistie, A. A., Yesuf, E. F., Zergaw, M. W., Getabalew, A., Mesekere, A. B., & Kefyalew, M. (2026). Artificial intelligence readiness among emergency department healthcare professionals: cross-sectional study from Ethiopia. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15503-0

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15503-0

Keywords: artificial intelligence, emergency medicine, Ethiopia, healthcare professionals, readiness, cross-sectional study, medical education, digital health, ethical awareness, health services research, Artificial, intelligence