Cross-country patterns in radiography student readiness for artificial intelligence

Artificial Intelligence (AI) is rapidly transforming radiographic practice by improving diagnostic accuracy, enhancing workflow efficiency, and supporting personalised care. Despite this growing relevance, limited research has explored radiography students' perceptions of AI, particularly within Arab academic institutions. This study examines radiography students' knowledge, attitudes, and perceptions of AI in medical imaging to identify educational gaps and guide curriculum development for effective AI integration.

A multi-national cross-sectional survey of 715 undergraduate radiography students from Egypt, Jordan, and the United Arab Emirates (UAE) was conducted using a validated 45-item questionnaire. Descriptive statistics were applied to assess knowledge, attitudes, and perceived barriers.

Only 27.8 % of participants had attended AI-related training, yet most reported moderate familiarity with AI. Students recognised AI's role in improving diagnostic accuracy and patient outcomes, and 61.8 % supported integrating AI education into undergraduate curricula. Concerns about job replacement were minimal, though barriers included limited access to AI tools, insufficient training, and inadequate expertise among academic staff.

Radiography students demonstrated a positive perception of AI and supported structured education on AI. However, institutional and infrastructural limitations remain.

These findings underscore the pressing need for structured AI curricula and academic staff training to equip students for evolving clinical roles. Policymakers should prioritize integrating AI education to ensure radiography graduates are ready for AI-enabled healthcare environments.