Enhancing medical imaging education: integrating computing technologies, digital image processing and artificial...

Area of Science:

• Medical Imaging Education
• Computational Techniques in Healthcare
• Artificial Intelligence Applications

Background:

• Technological advancements are transforming medical imaging (MI).
• Current undergraduate MI curricula may lack essential computational and AI components.
• There's a need to prepare MI professionals for evolving industry demands.

Purpose of the Study:

• To explore the integration of computing technologies, digital image processing, and AI into undergraduate MI education.
• To identify gaps and limitations in current MI educational practices.
• To propose a comprehensive curriculum framework for future-ready MI professionals.

Main Methods:

• Review of current educational practices in medical imaging.
• Analysis of existing curriculum gaps and limitations.
• Development of a proposed curriculum framework incorporating computational skills, image processing, and AI tools.

Main Results:

• Identification of key areas for curriculum enhancement in MI education.
• Proposal of a framework integrating Python, MATLAB, advanced image processing, and AI (e.g., ChatGPT).
• The framework aims to bridge the gap between academic training and professional practice.

Conclusions:

• Integrating computational skills, advanced image processing, and AI is crucial for modern MI education.
• The proposed curriculum framework can significantly improve MI education quality.
• This approach will better equip students for future challenges, enhancing diagnostic accuracy and workflow efficiency.