Knowledge, attitudes, and practices toward artificial intelligence in medicine among iranian physicians and medical students: a cross-sectional survey

Abstract

BACKGROUND: Artificial intelligence (AI) is rapidly transforming healthcare worldwide, yet the readiness of physicians and medical students to adopt AI-particularly in low- and middle-income settings-remains insufficiently understood. Examining knowledge, attitudes, and practices (KAP) toward AI is essential for informing educational priorities and implementation strategies. OBJECTIVES: This study aimed assessing AI-related knowledge, attitudes, and practices among Iranian physicians and medical students, examine demographic differences, and identify barriers to AI adoption. METHODS: A cross-sectional survey was conducted using an online convenience sampling approach across medical universities and clinical settings in Iran between June and August 2024. A validated questionnaire was developed based on a comprehensive literature review of existing KAP surveys related to artificial intelligence and digital health which assessed AI-related knowledge (3 items), attitudes (27 items), and practices (5 items). The reliability of the questionnaire was confirmed using Cronbach's alpha and ICC. Descriptive statistics and multivariable logistic regression were used to examine associations between demographic variables and KAP outcomes. Statistical significance was set at p-value < 0.05. RESULTS: A total of 238 participants completed the survey, including 165 physicians (69.7%) and 72 medical students (30.3%). Overall, 61.3% demonstrated good knowledge, 83.2% had positive attitudes, and 58.4% showed good AI-related practice. Medical students had higher mean knowledge scores than physicians (0.62 (SD = 0.32) vs. 0.58 (SD = 0.35); p = 0.025). Younger participants (18-25 years) showed higher mean knowledge than those aged > 65 years (0.70 (SD = 0.29) vs. 0.36 (SD = 0.36); p = 0.036). Although attitudes toward AI were uniformly positive (97.4% considered AI essential in medicine), practice levels were lower, particularly among participants aged 50-65 years (Mean practice score (SD) = 0.52 (0.21); p < 0.01). In adjusted analyses, age 50-65 years was independently associated with lower odds of good practice (AOR = 0.12; 95% CI: 0.04-0.41). CONCLUSIONS: Despite strong support for AI integration among Iranian physicians and medical students, gaps remain between positive attitudes and real-world AI use, especially among older clinicians. Structured, practice-oriented AI training and improved institutional infrastructure are needed to facilitate effective and equitable adoption of AI in Iran's healthcare system.

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