Mixed-methods study assesses AI-powered radiology systems adoption in Nairobi, suggesting need for policy support.
This study examines the adoption rate of AI-powered radiology systems among primary healthcare providers in Nairobi, Kenya. A mixed-methods approach was employed, including a survey of 150 randomly selected primary healthcare facilities and semi-structured interviews with key informants. Data analysis utilised descriptive statistics and thematic content analysis. In the survey, 32% of respondents reported using AI-powered radiology systems, while qualitative data highlighted common challenges such as high initial costs and lack of technical expertise. The adoption rate is relatively low, indicating potential for growth with targeted educational interventions and policy support. Primary healthcare facilities should be provided with subsidies to reduce the upfront cost of AI systems. Policymakers should mandate training programmes in AI technology within health education curricula. Treatment effect was estimated with logit(pᵢ)=β₀+β^ Xᵢ, and uncertainty reported using confidence-interval based inference.
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Ngugi wa Thiongo (2004) studied this question.
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