Abstract
The use of AI in the healthcare sector is facing some formidable concerns raised by the practitioners themselves. This study aimed to establish the concerns that surround the adoption of AI among Saudi Arabian healthcare professionals. Materials and methods: This was a cross-sectional study using stratified convenience sampling from September to November 2024 across health facilities. This study included all licensed healthcare professionals practicing for at least one year, whereas interns and administrative staff were excluded from the research. Data collection was conducted through a 33-item validated questionnaire that was provided in paper form and online. The questionnaire measured AI awareness with eight items, past experience with five items, and concerns in four domains represented by 20 items. Four hundred questionnaires were distributed, and the response rate was 78.5% (n = 314). The majority of the participants were females (52.5%), Saudis (89.2%), and employees of MOH (77.1%). The mean age for the participants was 35.6 ± 7.8 years. Quantitative analysis revealed high AI awareness scores with a mean of 3.96 ± 0.167, p < 0.001, and low previous experience scores with a mean of 2.65 ± 0.292. Data management-related worries came out as the top worry, with a mean of 3.78 ± 0.259, while the poor data entry impact topped with a mean of 4.15 ± 0.801; healthcare provider-related worries with a mean of 3.71 ± 0.182; and regulation/ethics-related worries with a mean of 3.67 ± 0.145. Health professionals' main concerns about AI adoption were related to data reliability and impacts on clinical decision-making, which significantly hindered successful AI integration in healthcare. These are the particular concerns that, if addressed through robust data management protocols and enhanced processes for clinical validation, will afford the best implementation of AI technology in an optimized way to bring better quality and safety to healthcare. Quantitative validation of AI outcomes and the development of standardized integration frameworks are subjects for future research.