Cross-country patterns in radiography student readiness for artificial intelligence

Area of Science:

• Medical Imaging Technology
• Radiography Education
• Artificial Intelligence in Healthcare

Background:

• Artificial Intelligence (AI) is revolutionizing radiographic practice, enhancing diagnostics and workflow.
• Limited research exists on radiography students' perceptions of AI, especially in Arab academic settings.
• This study addresses this gap by examining student knowledge, attitudes, and perceptions of AI in medical imaging.

Purpose of the Study:

• To assess radiography students' knowledge and attitudes towards AI in medical imaging.
• To identify perceptions and potential barriers to AI integration in radiography education.
• To inform curriculum development for effective AI implementation in radiography programs.

Main Methods:

• A multi-national cross-sectional survey was conducted with 715 undergraduate radiography students.
• Participants were recruited from Egypt, Jordan, and the United Arab Emirates (UAE).
• A validated 45-item questionnaire was used, followed by descriptive statistical analysis.

Main Results:

• Only 27.8% of students received AI training, despite moderate self-reported familiarity.
• A majority (61.8%) supported integrating AI education into undergraduate curricula.
• Key barriers identified include limited AI tool access, insufficient training, and inadequate academic staff expertise.

Conclusions:

• Radiography students generally hold positive views on AI and advocate for structured AI education.
• Institutional and infrastructural limitations currently impede widespread AI integration in radiography programs.
• There is a clear need for enhanced AI curricula and faculty training to prepare radiography graduates for AI-driven healthcare.