Exploring Medical Doctors' Confidence in Artificial Intelligence: The Role of Specialty, Experience, and Perceived...

Background: Artificial intelligence (AI) is increasingly integrated into healthcare, offering transformative potential across diagnostics, treatment, and clinical decision-making. As its adoption grows, understanding how medical doctors perceive and respond to AI, particularly in relation to their specialty, experience, and job security, is critical for effective implementation and acceptance. This study investigates the confidence of medical doctors in AI technologies and their role in healthcare, focusing on the impact of specialty, experience, and perceived job security. Method: A cross-sectional survey was conducted among 187 medical doctors across various specialties in Riyadh, Saudi Arabia, with a final sample of 176 participants. The survey assessed awareness, confidence, and concerns regarding AI integration into clinical practice. The survey was conducted across multiple healthcare hospitals in Riyadh, Saudi Arabia. Hospitals from both public and private sectors were included to ensure a diverse sample of healthcare professionals from different organizational structures. Results: A statistically significant association was found between specialty and confidence level (χ² = 14.5, p = 0.001). Among specialists, the majority (80%) reported high confidence in AI use compared to 45% of general practitioners and 38% of surgeons. Conversely, moderate confidence was most common among surgeons (46%), followed by general practitioners (35%) and specialists (13%). Additionally, participants with 11-20 years of experience reported the highest confidence, whereas those aged 55+ years showed the lowest perceived impact of AI on patient outcomes. Multivariate regression analysis identified specialty as the strongest predictor of AI confidence, with specialists being four times more likely to express high confidence in AI use (β = 0.89, p = 0.001) compared to general practitioners. Job displacement concerns negatively influenced confidence in AI, while age and years of experience had less impactful effects. Conclusions: The study concludes that addressing barriers to AI adoption will be crucial for enhancing its integration into healthcare and improving patient care. These findings underscore the importance of specialty-specific training and highlight the need for targeted educational programs, particularly for lower confidence groups such as general practitioners and surgeons. Lower confidence levels in these groups may result in a hesitant or incorrect use of AI tools, potentially compromising patient safety. Therefore, equipping all healthcare professionals with the necessary knowledge and confidence is essential for the safe and effective use of AI in clinical practice.