为医学教育中的人工智能伦理教学提出基于原则的方法。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES
Lukas Weidener, Michael Fischer
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引用次数: 0

摘要

非结构化:人工智能(AI)在医学中的应用有可能带来实质性的进步,例如改进诊断方法,近年来,科学界和社会对人工智能的关注与日俱增。然而,人工智能的使用也带来了新的伦理挑战,如增加偏见和潜在歧视患者的风险,以及可能导致过度诊断或诊断不足的误诊,给患者带来严重后果。认识到这些挑战,当前的研究强调了将人工智能伦理纳入医学教育的重要性。本研究旨在为人工智能伦理医学教育引入一套全面的伦理原则。这种以原则为基础的动态教学方法具有适应性和全面性,不仅能应对当前的伦理挑战,还能应对与人工智能在医学中的应用相关的新出现的伦理挑战。本研究对当前医学教育中有关人工智能伦理的学术讨论进行了理论分析,找出了潜在的差距和局限性。通过重点讨论人工智能医学和医学教育背景下的 "知情同意",说明了这些预期挑战的内在联系和跨学科性质。本研究提出了一种基于原则的人工智能伦理教育方法,它以医学伦理的四项原则--自主性、受益性、非恶意性和公正性--为基础,并通过整合三项公共卫生伦理原则进行扩展:效率、共同利益导向和相称性。本研究提出的基于原则的医学教育中人工智能伦理教学方法提供了一个基础框架,以应对当前学术讨论中建议的在医学中使用人工智能的预期伦理挑战。通过纳入公共卫生伦理学的三项原则,这种方法可以确保医学伦理学教育保持相关性,并对人工智能融入医学的动态环境做出反应。随着人工智能技术在医学领域的发展,医学伦理学教育必须做出相应的调整和发展。所提出的基于原则的医学教育人工智能伦理教学方法为确保未来的医学专业人员不仅了解人工智能在医学中的伦理层面,还能在实践中做出明智的伦理决策奠定了重要基础。未来的研究需要为拟议的基于原则的医学教育人工智能伦理教学方法制定基于问题和能力导向的学习目标和教育内容。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education.

The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increasing scientific and societal interest in recent years. However, the use of AI raises new ethical challenges, such as an increased risk of bias and potential discrimination against patients, as well as misdiagnoses potentially leading to over- or underdiagnosis with substantial consequences for patients. Recognizing these challenges, current research underscores the importance of integrating AI ethics into medical education. This viewpoint paper aims to introduce a comprehensive set of ethical principles for teaching AI ethics in medical education. This dynamic and principle-based approach is designed to be adaptive and comprehensive, addressing not only the current but also emerging ethical challenges associated with the use of AI in medicine. This study conducts a theoretical analysis of the current academic discourse on AI ethics in medical education, identifying potential gaps and limitations. The inherent interconnectivity and interdisciplinary nature of these anticipated challenges are illustrated through a focused discussion on "informed consent" in the context of AI in medicine and medical education. This paper proposes a principle-based approach to AI ethics education, building on the 4 principles of medical ethics-autonomy, beneficence, nonmaleficence, and justice-and extending them by integrating 3 public health ethics principles-efficiency, common good orientation, and proportionality. The principle-based approach to teaching AI ethics in medical education proposed in this study offers a foundational framework for addressing the anticipated ethical challenges of using AI in medicine, recommended in the current academic discourse. By incorporating the 3 principles of public health ethics, this principle-based approach ensures that medical ethics education remains relevant and responsive to the dynamic landscape of AI integration in medicine. As the advancement of AI technologies in medicine is expected to increase, medical ethics education must adapt and evolve accordingly. The proposed principle-based approach for teaching AI ethics in medical education provides an important foundation to ensure that future medical professionals are not only aware of the ethical dimensions of AI in medicine but also equipped to make informed ethical decisions in their practice. Future research is required to develop problem-based and competency-oriented learning objectives and educational content for the proposed principle-based approach to teaching AI ethics in medical education.

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来源期刊
JMIR Medical Education
JMIR Medical Education Social Sciences-Education
CiteScore
6.90
自引率
5.60%
发文量
54
审稿时长
8 weeks
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