Healthcare Professionals’ Acceptance of Artificial Intelligence Diagnostic Technologies in Saudi Hospitals: A Quantitative Study (The International Journal for Scientific Research, 2026)
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
Keywords: Artificial Intelligence; Medical Diagnosis; Technology Acceptance Model; Healthcare Professionals; Saudi Arabia; Vision 2030; Technology Adoption