Types, Utilisation, and Fascinating Factors Driving Dependence on AI-Based Self-Diagnostics among Students of the School of Health Technology, Akure

Abstract

Artificial Intelligence (AI) is rapidly transforming healthcare diagnostics, with global adoption in digital self-diagnosis projected to exceed US$11.5 billion by 2030. Yet, rising fascination and emotional dependence on AI raise ethical concerns over judgment erosion. This study examines how fascination, trust, and gender predict utilization and overreliance on AI diagnostic tools among students in Akure. A descriptive cross-sectional design was adopted, involving 458 students selected through opportunity (convenience) sampling and voluntary (self-selection) recruitment techniques from the School of Health Technology, Akure. Data were collected using a structured, validated questionnaire. Inferential analyses including Spearman’s Rank Correlation, Simple Linear Regression, and the Mann–Whitney U test were conducted using SPSS (v25) at a 0.05 significance level to examine relationships and group differences. The Spearman’s Rank Correlation revealed strong positive associations among fascination, trust, and utilization (ρ = .842, .755, .678; p < .001), indicating that greater fascination significantly enhances both trust and the frequency of AI tool use. The Simple Linear Regression showed that fascination strongly predicts AI dependence (R = .974, R² = .948, p < .001), with a one-unit increase in fascination producing a 0.783-unit rise in dependence, suggesting that emotional engagement is a powerful behavioural driver but may also induce automation bias. The Mann–Whitney U test found no significant gender difference in AI utilization (U = 25,637.50, p = .721), indicating that gender does not significantly influence self-diagnostic engagement. Fascination and trust are the central catalysts of AI adoption and utilisation among health students, yet they pose ethical risks of overreliance. Educational institutions, policymakers, and developers must therefore refocus from fascination-driven adoption toward competence-driven, critically reflective engagement to ensure safe and ethical integration of AI in health diagnostics.

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