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人工智能和机器学习在放射治疗中的影响:未来课程强化的考虑因素。

The Impact of Artificial Intelligence and Machine Learning in Radiation Therapy: Considerations for Future Curriculum Enhancement.

机构信息

School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.

School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.

出版信息

J Med Imaging Radiat Sci. 2020 Jun;51(2):214-220. doi: 10.1016/j.jmir.2020.01.008. Epub 2020 Feb 27.

Abstract

Artificial intelligence (AI) and machine learning (ML) approaches have caught the attention of many in health care. Current literature suggests there are many potential benefits that could transform future clinical workflows and decision making. Embedding AI and ML concepts in radiation therapy education could be a fundamental step in equipping radiation therapists (RTs) to engage in competent and safe practice as they utilise clinical technologies. In this discussion paper, the authors provide a brief review of some applications of AI and ML in radiation therapy and discuss pertinent considerations for radiation therapy curriculum enhancement. As the current literature suggests, AI and ML approaches will impose changes to routine clinical radiation therapy tasks. The emphasis in RT education could be on critical evaluation of AI and ML application in routine clinical workflows and gaining an understanding of the impact on quality assurance, provision of quality of care and safety in radiation therapy as well as research. It is also imperative RTs have a broader understanding of AI/ML impact on health care, including ethical and legal considerations. The paper concludes with recommendations and suggestions to deliberately embed AI and ML aspects in RT education to empower future RT practitioners.

摘要

人工智能(AI)和机器学习(ML)方法引起了医疗保健领域许多人的关注。目前的文献表明,有许多潜在的好处可以改变未来的临床工作流程和决策制定。将 AI 和 ML 概念嵌入放射治疗教育中,可能是为放射治疗师(RTs)配备能力和安全实践的基本步骤,因为他们利用临床技术。在本文中,作者简要回顾了 AI 和 ML 在放射治疗中的一些应用,并讨论了放射治疗课程增强的相关考虑因素。正如目前的文献表明的那样,AI 和 ML 方法将对常规临床放射治疗任务产生影响。RT 教育的重点可能是对常规临床工作流程中 AI 和 ML 应用的批判性评估,并了解其对放射治疗质量保证、护理质量提供以及安全性的影响,以及研究。同样至关重要的是,RTs 对 AI/ML 对医疗保健的影响有更广泛的了解,包括伦理和法律方面的考虑。本文最后提出了建议和建议,旨在将 AI 和 ML 方面有意地嵌入 RT 教育中,以增强未来 RT 从业者的能力。

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