Investigating the Potential of Generative AI Clinical Case-Based Simulations on Radiography Education: A Pilot Study

Area of Science:

• Medical Imaging Education
• Artificial Intelligence in Healthcare
• Radiography Professional Development

Background:

• Regional and rural medical imaging education faces resource limitations.
• Generative AI offers a potential solution for radiographer training.
• AI can support professional development and overcome educational barriers.

Purpose of the Study:

• To evaluate the educational value of an in-house AI-based imaging simulation tool.
• To assess the impact of AI-generated content on radiography students and recent graduates.
• To gauge interest in AI for medical imaging education and development.

Main Methods:

• A pilot study delivered a lecture with AI-generated medical imaging content.
• Surveys assessed participants' understanding, perceptions, and interest in AI.
• Participants included radiography students and recent graduates.

Main Results:

• Participants had foundational awareness of AI in medical imaging.
• Graduates expected higher clinical realism in AI simulations.
• AI simulators showed pedagogical value for basic anatomical education.

Conclusions:

• AI-driven imaging simulation can enhance radiography education and reduce barriers.
• Further development is needed to improve clinical fidelity.
• AI tools can support foundational training and foster AI innovation engagement.