Healthcare Professionals’ Acceptance of Artificial Intelligence Diagnostic Technologies in Saudi Hospitals: A Quantitative Study

Abstract

This study aims to measure the level of acceptance of artificial intelligence (AI) diagnostic technologies among healthcare professionals in Saudi Arabian hospitals, and to identify the key factors influencing such acceptance. Grounded in the Technology Acceptance Model (TAM) by Davis (1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT) by Venkatesh et al. (2003), the study employed a quantitative descriptive-analytical approach. Data were collected via an electronic questionnaire administered to a sample of 50 healthcare professionals working in both governmental and private hospitals in Saudi Arabia during February–March 2026.

Results revealed an overall acceptance mean of 3.78 out of 5.0, indicating a conditionally positive disposition toward AI in medical diagnosis. The five primary constructs demonstrated varying levels: Perceived Usefulness (M=3.745), Perceived Ease of Use (M=3.740), Trust in AI outputs (M=4.010), Training Readiness (M=3.060), and Behavioral Intention (M=3.630). Cronbach’s alpha for the overall scale reached 0.902, confirming excellent internal consistency. Multiple regression analysis revealed that Trust (β=0.379, p<0.001), Perceived Usefulness (β=0.413, p<0.001), and Perceived Ease of Use (β=0.310, p<0.001) were the strongest predictors of acceptance, with the model explaining 96.3% of variance in overall acceptance (R²=0.963).

A critical training gap was identified: while professionals expressed high training need (M=3.680), actual training received was considerably lower (M=2.440), yielding a gap of 1.240 points—the most significant structural barrier to AI adoption. One-way ANOVA revealed statistically significant differences in acceptance by professional specialty (F=2.714, p=0.017, η²=0.194), with physicians scoring highest (M=4.009) and health technicians lowest (M=2.955). No significant difference was found between governmental and private hospital staff (t=−0.912, p=0.366). The study recommends implementing mandatory AI training programs tailored by specialty, prioritizing explainable AI systems in procurement, establishing clear regulatory frameworks, and embedding AI readiness metrics within Saudi Vision 2030 health transformation indicators.