Perceived Worries in the Adoption of Artificial Intelligence Among Healthcare Professionals in Saudi Arabia: A Cross-Sectional Survey Study
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View abstract on PubMed
Summary
This summary is machine-generated.Saudi healthcare professionals show high artificial intelligence (AI) awareness but have concerns about data reliability and clinical decision-making impacts. Addressing these issues is key for successful AI integration in healthcare.
Area Of Science
- Healthcare Informatics
- Medical Artificial Intelligence
- Health Policy and Management
Background
- Artificial intelligence (AI) adoption in healthcare is growing, yet significant concerns exist among practitioners.
- Understanding these concerns is crucial for the effective integration of AI technologies in clinical settings.
- Saudi Arabian healthcare professionals' perspectives on AI adoption remain largely unexplored.
Purpose Of The Study
- To identify and analyze the specific concerns of Saudi Arabian healthcare professionals regarding the adoption of AI.
- To assess the level of AI awareness and previous experience among these professionals.
- To provide insights for optimizing AI implementation in Saudi Arabian healthcare.
Main Methods
- A cross-sectional study conducted from September to November 2024.
- Stratified convenience sampling included licensed healthcare professionals with at least one year of practice.
- A 33-item validated questionnaire assessed AI awareness, experience, and concerns across four domains (data management, data entry impact, provider-related, regulation/ethics).
Main Results
- High AI awareness (mean=3.96) but low previous experience (mean=2.65) were observed.
- Top concerns included data management (mean=3.78), poor data entry impact (mean=4.15), provider-related issues (mean=3.71), and regulation/ethics (mean=3.67).
- Data reliability and impact on clinical decision-making emerged as primary barriers to AI integration.
Conclusions
- Healthcare professionals' concerns about data reliability and clinical decision-making significantly hinder AI adoption.
- Addressing these concerns through robust data management and clinical validation is essential for successful AI implementation.
- Future research should focus on quantitative validation of AI outcomes and developing standardized integration frameworks.
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