Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs.
Artificial Intelligence Education for the Health Workforce: Expert Survey of Approaches and Needs.
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DOI：
10.2196/35223
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发表时间：
2022-04-04
影响因子：
3.6
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中科院分区：
文献类型：
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随着人工智能应用在各种护理环境和专业领域的出现，当前和未来的卫生人力为在医疗保健中使用人工智能 (AI) 的可能性做好准备越来越受到关注。目前，教育工作者对于需要学习什么或如何支持或评估这种学习还没有达成明显的共识。我们的研究旨在探索医疗保健教育专家的想法和计划，以帮助卫生人员做好与人工智能合作的准备，并确定整个国家医疗保健系统中课程和教育资源的关键差距。一项调查就教育策略、主题优先事项、有意义的学习活动、期望的态度和技能等方面征求了专家对卫生人员人工智能教育的看法。 2020 年底，来自澳大利亚不同卫生人力小组的总共 39 名老年人提供了评级和自由文本回复。这些回复强调了道德影响教育的重要性、大数据集在人工智能临床应用中的适用性、机器学习原理、人工智能的具体诊断和治疗应用、临床工作期间认知负荷的改变以及临床环境中人与机器之间的互动。受访者还概述了实施的障碍，例如缺乏治理结构和流程、资源限制和文化调整。本次调查中报告的世界各地的进一步工作可以帮助负责准备卫生人力的教育工作者和教育当局最大限度地降低风险并实现在医疗保健中实施人工智能的好处。
The preparation of the current and future health workforce for the possibility of using artificial intelligence (AI) in health care is a growing concern as AI applications emerge in various care settings and specializations. At present, there is no obvious consensus among educators about what needs to be learned or how this learning may be supported or assessed. Our study aims to explore health care education experts’ ideas and plans for preparing the health workforce to work with AI and identify critical gaps in curriculum and educational resources across a national health care system. A survey canvassed expert views on AI education for the health workforce in terms of educational strategies, subject matter priorities, meaningful learning activities, desired attitudes, and skills. A total of 39 senior people from different health workforce subgroups across Australia provided ratings and free-text responses in late 2020. The responses highlighted the importance of education on ethical implications, suitability of large data sets for use in AI clinical applications, principles of machine learning, and specific diagnosis and treatment applications of AI as well as alterations to cognitive load during clinical work and the interaction between humans and machines in clinical settings. Respondents also outlined barriers to implementation, such as lack of governance structures and processes, resource constraints, and cultural adjustment. Further work around the world of the kind reported in this survey can assist educators and education authorities who are responsible for preparing the health workforce to minimize the risks and realize the benefits of implementing AI in health care.