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Abstract：
OBJECTIVE: To provide a guideline curriculum related to Artificial Intelligence (AI), for the education and training of European Medical Physicists (MPs).
METHODS: The proposed curriculum consists of two levels: Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology). The learning outcomes of the training are presented as knowledge, skills and competences (KSC approach).
RESULTS: For the Basic section, KSCs were stratified in four subsections: (1) Medical imaging analysis and AI Basics; (2) Implementation of AI applications in clinical practice; (3) Big data and enterprise imaging, and (4) Quality, Regulatory and Ethical Issues of AI processes. For the Advanced section instead, a common block was proposed to be further elaborated by each subspecialty core curriculum. The learning outcomes were also translated into a syllabus of a more traditional format, including practical applications.
CONCLUSIONS: This AI curriculum is the first attempt to create a guideline expanding the current educational framework for Medical Physicists in Europe. It should be considered as a document to top the sub-specialties\' curriculums and adapted by national training and regulatory bodies. The proposed educational program can be implemented via the European School of Medical Physics Expert (ESMPE) course modules and - to some extent - also by the national competent EFOMP organizations, to reach widely the medical physicist community in Europe.
METHODS: The proposed curriculum consists of two levels: Basic (introducing MPs to the pillars of knowledge, development and applications of AI, in the context of medical imaging and radiation therapy) and Advanced. Both are common to the subspecialties (diagnostic and interventional radiology, nuclear medicine, and radiation oncology). The learning outcomes of the training are presented as knowledge, skills and competences (KSC approach).
RESULTS: For the Basic section, KSCs were stratified in four subsections: (1) Medical imaging analysis and AI Basics; (2) Implementation of AI applications in clinical practice; (3) Big data and enterprise imaging, and (4) Quality, Regulatory and Ethical Issues of AI processes. For the Advanced section instead, a common block was proposed to be further elaborated by each subspecialty core curriculum. The learning outcomes were also translated into a syllabus of a more traditional format, including practical applications.
CONCLUSIONS: This AI curriculum is the first attempt to create a guideline expanding the current educational framework for Medical Physicists in Europe. It should be considered as a document to top the sub-specialties\' curriculums and adapted by national training and regulatory bodies. The proposed educational program can be implemented via the European School of Medical Physics Expert (ESMPE) course modules and - to some extent - also by the national competent EFOMP organizations, to reach widely the medical physicist community in Europe.
摘要：
目标：提供与人工智能（AI）相关的指南课程，用于欧洲医学物理学家（MP）的教育和培训。
方法：拟议的课程包括两个级别：基本（将MP引入知识的支柱，人工智能的发展和应用，在医学成像和放射治疗的背景下)和高级。两者都是亚专科(诊断和介入放射学，核医学,和放射肿瘤学）。培训的学习成果以知识的形式呈现，技能和能力(KSC方法)。
结果：对于基本部分，KSC分为四个小节：（1）医学影像分析和AI基础知识；（2）在临床实践中实施AI应用；（3）大数据和企业影像，（4）质量，人工智能流程的监管和道德问题。对于“高级”部分，建议每个子专业核心课程进一步阐述一个共同的模块。学习成果也被翻译成更传统格式的教学大纲，包括实际应用。
结论：该AI课程是首次尝试创建指南，以扩展欧洲医学物理学家的当前教育框架。应将其视为子专业课程的顶部文件，并由国家培训和监管机构进行调整。拟议的教育计划可以通过欧洲医学物理专家学院（ESMPE）课程模块实施，并且在某种程度上也可以由国家主管的EFOMP组织实施。广泛接触欧洲的医学物理学家社区。
方法：拟议的课程包括两个级别：基本（将MP引入知识的支柱，人工智能的发展和应用，在医学成像和放射治疗的背景下)和高级。两者都是亚专科(诊断和介入放射学，核医学,和放射肿瘤学）。培训的学习成果以知识的形式呈现，技能和能力(KSC方法)。
结果：对于基本部分，KSC分为四个小节：（1）医学影像分析和AI基础知识；（2）在临床实践中实施AI应用；（3）大数据和企业影像，（4）质量，人工智能流程的监管和道德问题。对于“高级”部分，建议每个子专业核心课程进一步阐述一个共同的模块。学习成果也被翻译成更传统格式的教学大纲，包括实际应用。
结论：该AI课程是首次尝试创建指南，以扩展欧洲医学物理学家的当前教育框架。应将其视为子专业课程的顶部文件，并由国家培训和监管机构进行调整。拟议的教育计划可以通过欧洲医学物理专家学院（ESMPE）课程模块实施，并且在某种程度上也可以由国家主管的EFOMP组织实施。广泛接触欧洲的医学物理学家社区。
