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

Sibusiso Mdletshe1, Alan Wang2,3,4,5

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This summary is machine-generated.

This paper proposes an updated medical imaging (MI) curriculum integrating computing, digital image processing, and artificial intelligence (AI). The goal is to equip future MI professionals with essential skills for enhanced diagnostic accuracy and workflow efficiency.

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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.