Knowledge, perceptions, and interest of referring physicians regarding the implementation of Artificial Intelligence in CT Imaging in the Democratic Republic of Congo - Mazoba, Tacite Kpanya; Lombo, Benjamin Bofengu; Luyeye, Gertrude Mvila; Mukaya, Jean Tshibola; Molua, Antoine Aundu (Orapuh Journal, 2025)
Introduction Artificial intelligence (AI) is increasingly integrated into medical imaging, including computed tomography (CT); however, its uptake remains limited in low-resource settings such as the Democratic Republic of Congo (DRC). Understanding physicians’ knowledge, perceptions, and interest is essential for guiding effective implementation strategies. Purpose To assess levels of knowledge, perception, and interest regarding AI integration in CT imaging among physicians in the DRC, and to identify relevant demographic and professional correlates. Methods A cross-sectional electronic survey was conducted between September and December 2024 among 740 physicians across the DRC. The questionnaire captured sociodemographic information and assessed AI-related knowledge (using a scored set of objective items, with ≥50% considered acceptable), perception, and interest (using 5-point Likert scales). Bivariate analyses (Chi-square and t-tests) and multivariate logistic regressions were used to identify predictors of high knowledge, favourable perception, and strong interest. Results Among participants, 64.9% were aged 35 years or younger, 67.6% were male, 70.9% practised in Kinshasa, and 65.1% were general practitioners. Acceptable knowledge was observed in 54.7% of respondents. Favourable perception and strong interest were reported by 46.2% and 66.8% of respondents, respectively. In adjusted analyses, being under 36 years, having ≤15 years of professional experience, working as a general practitioner, and practising in a provincial setting were significantly associated with higher levels of knowledge, perception, or interest (p < .05). Conclusion Early-career physicians, general practitioners, and those practising outside the capital appear more receptive to AI in CT imaging. These findings highlight the importance of targeted training initiatives and policy engagement to promote equitable and effective AI integration in medical imaging across the DRC.