Technical University of Munich.
University of Cambridge.
Am J Bioeth. 2022 Jul;22(7):4-20. doi: 10.1080/15265161.2022.2040647. Epub 2022 Mar 16.
Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress' prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence?
机器智能已经帮助医务人员完成了许多任务。然而，道德决策还没有交给计算机。在这项概念验证研究中，我们展示了如何使用基于 Beauchamp 和 Childress 的初步原则的算法来为医疗机构中发生的一系列道德困境提供建议。我们解释了为什么选择模糊认知图来建立咨询系统，以及如何利用机器学习来训练它。我们报告了将善行、不伤害和患者自主权原则付诸实践的艰巨任务，并描述了如何选择从临床案例训练数据集中提取的合适输入参数。初步的性能结果是有希望的，但道德的算法方法也存在一些弱点和局限性。我们真的应该将临床伦理的敏感领域交给机器智能吗？
