Awareness, perceptions, and educational preferences for AI among radiography students: A cross sectional survey.
Authors
Affiliations (6)
Affiliations (6)
- Department of Medical Imaging, Faculty of Applied Medical Sciences, The Hashemite University, Zarqa, 13133, Jordan.
- Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, 22110, Jordan.
- Department of Accident and Emergency Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, 22110, Jordan.
- Department of Medical Physics, College of Applied Sciences, University of Fallujah, Iraq.
- School of Physics, Universiti Sains Malaysia, 11700, Penang, Malaysia.
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland. Electronic address: [email protected].
Abstract
Artificial intelligence (AI) is gaining increasing significance in radiography. However, undergraduate radiography students often have limited exposure to AI principles and an incomplete understanding of its capabilities and limitations. This study evaluated radiography students' self-reported familiarity with AI, perceptions of its role in medical imaging, and educational preferences. A descriptive cross-sectional survey was conducted at a single institution in Jordan between March and August 2024. A self-administered, in-person paper questionnaire, adapted from a previously published survey, was distributed to students attending scheduled teaching sessions. Responses were analysed descriptively using frequencies and percentages. Of the 217 students approached, 180 participated (response rate 82.9%). Only 46.1% of respondents reported good familiarity with AI. Most students perceived AI as having useful applications in medical imaging and supported its integration into university training. However, when AI output conflicted with human judgement, 60.0% of students indicated that they would seek expert opinion, reflecting a cautious approach to AI-supported decision-making. Radiography students reported positive attitudes toward AI and strong support for curricular integration, alongside caution about routine reliance on AI for decision making. Structured educational activities that provide practical exposure, clarify appropriate use cases, and emphasise human oversight may help students use AI safely and effectively in future practice. Artificial intelligence is being used more often in medical imaging, but students may not fully understand it. This study surveyed radiography students in Jordan to understand their knowledge of artificial intelligence, how they view its role, and how they want to learn about it. This study found that students welcomed its use but had limited understanding and preferred to seek expert advice when unsure. This matters because better teaching can help students use these tools safely and confidently in practice.