Prevalence, Patterns, and Determinants of Artificial Intelligence Use Among Healthcare Professionals in Lagos, Nigeria: A Cross Sectional Study

Abstract

<title>Abstract</title> <bold>Background:</bold> Artificial Intelligence (AI) is rapidly transforming healthcare delivery worldwide. However, its adoption, patterns of use, and associated factors among healthcare professionals in Nigeria remain underexplored. This study assessed the prevalence, patterns, and determinants of AI use among healthcare professionals in Lagos State, Nigeria. <bold>Methods:</bold> A descriptive cross-sectional study was conducted between May and August 2024 among 415 healthcare professionals, selected using stratified random sampling from tertiary, secondary, and primary government-owned health facilities. Data were collected usingself-administered, structured questionnaires and analysed using SPSS. Descriptive statistics, chi-square tests, and logistic regression were employed. <bold>Results:</bold> Despite only 19.8% receiving formal AI training, 86.5% of respondents reported using AI, with 51.3% applying it to professional duties - mainly for diagnostic support. AI use in professional duties was significantly associated with educational level (<italic>p</italic> = 0.042) and showed a borderline association with facility type (<italic>p </italic>= 0.055). Those with postgraduate degrees were twice as likely to use AI compared to those with a bachelor’s degree or lower (OR = 2.07, <italic>p</italic> = 0.014), while professionals at secondary facilities were approximately1.5 times more likely to use AI than those at primary facilities (OR = 1.55, <italic>p</italic> = 0.056). The main motivators included convenience and efficiency, and the barriers included data privacy concerns and distrust in AI. <bold>Conclusion:</bold> AI use is widespread among healthcare professionals in Lagos, Nigeria,despite limited formal training, with education level and facility type influencing professional application. Targeted training, improved digital infrastructure, and clear data protection policies are recommended to foster equitable AI integration at all levels of care.

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