This study examines
the feasibility, adoption drivers, and readiness for the deployment of an
AI-powered stroke detection platform in Ethiopia, an emerging market with
severe radiologist shortages. AI’s potential in radiology, especially for
stroke detection, has been explored in several developing and emerging
countries. In Ethiopia, though, this is the first study of its kind. Guided by
Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) frameworks,
we analyze survey data from healthcare stakeholders to quantify adoption
readiness and to identify key contextual drivers. Descriptive results indicate
that approximately 92% of respondents express willingness to pilot AI
diagnostics. Advanced analyses, including a multivariable logistic regression,
reveal that willingness to join a pilot and perceived usefulness are the
strongest predictors of adoption intention, with pilot willingness associated
with a nearly threefold higher likelihood of adoption. Findings suggest that
contextual enablers such as affordability and design alignment with local needs
(like local language support and offline functionality) are central to
perceived ease and relative advantage, while trust and clinical validation
shape overall adoption. This study concludes that Ethiopia offers a viable early-stage
market for AI-driven diagnostic tools, driven primarily by perceived value and
affordability rather than technical barriers. The research contributes
actionable insights into how affordability, user-friendliness, and contextual
adaptation can accelerate responsible AI deployment to bridge healthcare access
gaps in resource-constrained emerging markets.
