Mdletshe Sibusiso, Wang Alan
Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
J Med Radiat Sci. 2025 Mar;72(1):148-155. doi: 10.1002/jmrs.837. Epub 2024 Nov 7.
The rapid advancement of technology has brought significant changes to various fields, including medical imaging (MI). This discussion paper explores the integration of computing technologies (e.g. Python and MATLAB), digital image processing (e.g. image enhancement, segmentation and three-dimensional reconstruction) and artificial intelligence (AI) into the undergraduate MI curriculum. By examining current educational practices, gaps and limitations that hinder the development of future-ready MI professionals are identified. A comprehensive curriculum framework is proposed, incorporating essential computational skills, advanced image processing techniques and state-of-the-art AI tools, such as large language models like ChatGPT. The proposed curriculum framework aims to improve the quality of MI education significantly and better equip students for future professional practice and challenges while enhancing diagnostic accuracy, improving workflow efficiency and preparing students for the evolving demands of the MI field.
技术的飞速发展给包括医学成像(MI)在内的各个领域带来了重大变革。本讨论文件探讨了将计算技术(如Python和MATLAB)、数字图像处理(如图像增强、分割和三维重建)以及人工智能(AI)融入本科医学成像课程的情况。通过审视当前的教育实践,找出了阻碍培养适应未来需求的医学成像专业人员的差距和局限性。提出了一个全面的课程框架,纳入了基本的计算技能、先进的图像处理技术和诸如ChatGPT等大型语言模型之类的前沿AI工具。拟议的课程框架旨在显著提高医学成像教育的质量,使学生更好地为未来的专业实践和挑战做好准备,同时提高诊断准确性、改善工作流程效率,并使学生为医学成像领域不断变化的需求做好准备。