Artificial Intelligence in Primary Care Decision-Making: Survey of Healthcare Professionals in Saudi Arabia

Artificial intelligence (AI) has the potential to revolutionize healthcare, particularly in primary care, by improving clinical decision-making and patient outcomes. AI technologies, such as machine learning and natural language processing, can assist clinicians in diagnosing conditions, predicting outcomes, recommending treatments, and identifying at-risk individuals. Despite its potential, AI adoption in primary care is slow due to various challenges, including resource limitations, clinician training, and concerns about the reliability of AI systems. Understanding healthcare professionals' perceptions of AI is crucial for overcoming these barriers and promoting its integration into clinical practice.

A cross-sectional, survey-based study was conducted to assess healthcare professionals' awareness, usage, perceptions, and barriers to AI adoption in primary care decision-making in Saudi Arabia. The study included 250 healthcare professionals from primary care settings across urban, rural, and hospital-based clinics. Data were collected via an electronic survey that included both quantitative and qualitative questions, and analyzed using descriptive and inferential statistics.

A total of 250 healthcare professionals participated in the survey. The majority were primary care physicians (44.8%), with the remaining participants consisting of nurses (27.2%), medical assistants (15.6%), and healthcare administrators (8.8%). Awareness of AI tools was mixed, with 14.8% of respondents very familiar with AI and 47.2% unfamiliar. Thirty-one percent of respondents reported using AI tools, primarily for diagnostic support (59.5%). Common barriers to AI adoption included high implementation costs (49.2%) and lack of training (34%). A significant portion of respondents (48%) expressed concerns about AI undermining the human touch in healthcare.

AI adoption in primary care is hindered by low familiarity and usage, as well as several barriers, including cost, lack of training, and concerns about the reliability of AI systems. However, there is optimism about AI's potential to support clinical decision-making. Overcoming these barriers through targeted education, infrastructure investment, and further research is essential for promoting AI integration in primary care and realizing its potential benefits for clinical practice.