Attitudes, perceptions, and factors influencing the adoption of artificial intelligence Among healthcare professionals in Saudi Arabia: a UTAUT-based study

Abstract

Background Amid the global race toward intelligent healthcare systems, Saudi Arabia stands at a pivotal moment in its digital health transformation. Understanding how prepared healthcare professionals are to adopt artificial intelligence is essential for shaping successful national strategies. Objectives This study aimed to assess healthcare professionals’ attitudes, perceptions, and intentions toward using AI in clinical practice; examine awareness and actual use; identify key predictors of AI adoption based on the Unified Theory of Acceptance and Use of Technology (UTAUT); and explore the mediating role of institutional support. Methods A cross-sectional survey was conducted among 521 healthcare professionals, including physicians, nurses, administrators, and allied health workers, across Saudi Arabia. The survey assessed awareness, usage, perceived usefulness, ease of use, social influence, facilitating conditions, perceived risks, and the intention to use AI. Data were analyzed using chi-squared tests, multiple regression, and mediation analyses. Results Although AI awareness was remarkably high (89.1%) and optimism toward the future was strong (79.0%), only 51.2% of participants reported actual clinical use of AI, A 37.9 percentage-point awareness–use gap. Two factors consistently stood out as powerful drivers of intention: believing that AI is genuinely useful (B = 0.491, p < .001) and feeling confident in one's ability to use it (B = 0.224, p < .001). Institutional support played an important but mostly indirect role in shaping intentions by enhancing these two beliefs. Social influence had little effect and was negative for nurses, whereas perceived risk did not significantly deter adoption. Despite structural and ethical challenges, intention to use AI remained high (71.6%), A 20.4 percentage-point gap ahead of actual use, indicating that organizational barriers, rather than individual willingness, remain the primary obstacle to AI integration. Conclusion These dynamics provide unique opportunities. With 71.6% of professionals intending to adopt AI, targeted training initiatives, clearer governance frameworks, and organizational support may help facilitate the sustainable integration of AI into healthcare practice. This study offers empirical evidence and a roadmap for transforming enthusiasm into sustainable, safe, and meaningful AI integration, and may support healthcare leaders and policymakers in developing strategies for safe and sustainable AI integration within the Saudi healthcare system. Practical Implications Advancing AI-enabled healthcare in Saudi Arabia requires investment not only in technology, but also in healthcare professionals’ preparedness and organizational support. Structured training programs, hands-on exposure to AI tools, supportive leadership, adequate infrastructure, and clear data governance frameworks may help healthcare professionals adopt AI more confidently and sustainably within clinical practice.