Attitudes and perceptions toward artificial intelligence among practicing rheumatologists in Iraq: a cross-sectional web-based survey

Artificial intelligence is becoming a bigger part of healthcare, particularly in rheumatology, where clinicians must integrate complex clinical, laboratory, and imaging data. Physicians' perceptions of AI may substantially influence the extent to which it is implemented responsibly in routine clinical practice.

This study aimed to assess the attitudes of practicing rheumatologists in Iraq toward artificial intelligence and to examine their association with demographic and professional characteristics.

A four-month web-based cross-sectional survey was conducted from October 2025 to January 2026. A questionnaire adapted from previous studies was refined through expert review and pilot testing before distribution. The questionnaire comprised demographic information, attitude statements measured on a five-point Likert scale, and items assessing general perceptions of artificial intelligence. Composite attitude scores were categorized using modified Bloom's cutoff criteria. Statistical analysis was performed using SPSS and R.

A total of 162 complete responses were collected. Most respondents were aged 35-44 years (42.0%), male (56.8%), and specialists (67.9%). Overall, 61.7% reported familiarity with AI and 69.1% had already used AI in rheumatology practice. Most participants 56.8% exhibited a poor attitude, 32.1% a moderate attitude, and 11.1% a good attitude toward AI. Bivariate analysis identified familiarity with AI ( $p=0.003$ ) and involvement in research ( $p=0.009$ ) as significantly associated with more favorable attitudes.

Iraqi rheumatologists demonstrate cautious engagement with artificial intelligence, with the majority displaying poor-to-moderate overall attitude scores on the standardized composite scale. Greater familiarity with AI and research involvement were associated with more favorable attitudes, highlighting the importance of targeted digital literacy and specialty-specific education to support effective AI adoption in clinical practice.