Clement David-Olawade, Aanuoluwapo, Wada, Ojima Z., Adeniji, Yinka Julianah ORCID: https://orcid.org/0000-0001-5809-4888, Aderupoko, Ibukunoluwa Victoria and Olawade, David ORCID: https://orcid.org/0000-0003-0188-9836 (2025) Artificial intelligence readiness among healthcare students in Nigeria: A cross-sectional study assessing knowledge gaps, exposure, and adoption willingness. International Journal of Medical Informatics, 204. p. 106085.

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Abstract

Background
Artificial intelligence (AI) is rapidly transforming healthcare globally, yet its adoption in developing countries remains limited. As future practitioners, the readiness of healthcare students is crucial for successful AI integration, but this remains unexplored in the Nigerian context.
Objectives
This study aimed to assess AI readiness among healthcare students at a major Nigerian university by evaluating their foundational knowledge, practical exposure, and willingness to adopt AI technologies in clinical practice.
Methods
A cross-sectional study was conducted among 551 healthcare students at Obafemi Awolowo University using a semi-structured, validated questionnaire. The instrument utilized distinct sections with open-ended questions to objectively measure AI knowledge, assess exposure to AI applications, and gauge attitudes toward AI adoption. Data were analyzed using descriptive statistics and one-way ANOVA, with statistical significance set at p < 0.05.
Results
A significant knowledge-perception paradox emerged: while 60 % of students believed they had high AI knowledge; objective assessment showed 92 % had low knowledge levels. Foundational concepts were poorly understood, with only 12 % correctly defining machine learning. Despite this, students expressed overwhelmingly positive attitudes, with 90.8 % believing AI would improve workflow efficiency and 84.4 % willing to undertake AI training. Practical exposure to AI was minimal, with electronic record keeping being the most frequently encountered application (43.4 %). Knowledge levels were significantly associated with willingness to adopt AI (p < 0.05), as students with higher knowledge showed greater confidence but also a more critical awareness of AI’s limitations.
Conclusion
Nigerian healthcare students show strong enthusiasm for AI adoption but have significant knowledge gaps and limited practical exposure. However, substantial concerns exist regarding the translation of expressed willingness into actual practice, particularly among early-year students who lack clinical exposure to understand AI limitations, bias, and real-world implementation challenges. These findings highlight an urgent need for AI curriculum integration and infrastructure development to prepare future healthcare professionals for an increasingly AI-driven healthcare landscape.

Item Type:	Article
Status:	Published
DOI:	10.1016/j.ijmedinf.2025.106085
School/Department:	London Campus
URI:	https://ray.yorksj.ac.uk/id/eprint/12552

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