用于放射学应用的教学AI:来自AAPM,ACR,RSNA和SIIM的多社会推课程
Felipe Kitamura1,2,3, Timothy Kline1,4, Daniel Warren5
1Society of Imaging Informatics in Medicine, Leesburg, USA.
Medical physics
|October 1, 2025
概括
医学成像正在与人工智能一起发展,但复杂性带来了挑战. 专业协会制定了一个能力课程,以确保在医疗保健中安全有效地使用AI.
科学领域:
- 医学成像医学成像
- 机器学习 机器学习
- 人工智能的人工智能是人工智能.
背景情况:
- 由于机器学习 (ML) 和计算能力的增加,医疗成像正在迅速发展.
- ML算法在疾病检测,诊断,预后和治疗方面提供了潜在的改进.
- 挑战包括ML模型的复杂性,数据要求和潜在的偏差,阻碍安全采用.
研究的目的:
- 为了应对在医学成像中采用ML的挑战.
- 为与人工智能系统交互的医学成像专业人员开发一个能力课程.
- 为用户,购买者,临床合作者和AI系统开发人员提供指导.
主要方法:
- 主要医学成像专业协会 (AAPM,ACR,RSNA,SIIM) 之间的合作.
- 制定一个大纲,概述推的能力.
- 专注于不同职业角色的特定任务需求.
主要成果:
- 一个课程详细说明了医学成像专业人员与人工智能接触的能力.
- 该课程针对四个关键人物:用户,采购者,临床合作者和开发者.
- 该框架强调安全有效地使用机器学习系统.
结论:
- 能力课程旨在加强在医学成像中安全有效地使用ML.
- 通过解决角色特定需求,该指南支持改善患者护理.
- 这个课程提供了一个基础框架,而不是一个规范性的课程.
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