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面向日常工作中的重症监护医生的人工智能伦理

AI ethics for the everyday intensivist.

作者信息

Tan Sing Chee, Modra Lucy, Hensman Tamishta

机构信息

Northern Health, Australia.

Austin Health, Australia.

出版信息

Crit Care Resusc. 2025 Jun 26;27(2):100115. doi: 10.1016/j.ccrj.2025.100115. eCollection 2025 Jun.

DOI:10.1016/j.ccrj.2025.100115
PMID:40677679
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12268048/
Abstract

In Australian intensive care units (ICUs), Artificial Intelligence (AI) promises to enhance efficiency and improve patient outcomes. However, ethical concerns surrounding AI must be addressed before widespread adoption. We examine the ethical challenges of of AI using the framework of the four pillars of biomedical ethics-beneficence, nonmaleficence, autonomy, and justice, and discuss the need for a fifth pillar of explicability. We consider the risks of perpetuating inequities, privacy breaches, and unintended harms, particularly in disadvantaged populations such as First Nations people. We advocate for a national strategy for ICUs to guide the ethical implementation of AI, that aligns with existing National AI Frameworks. Our recommendations for implementation of safe and ethical AI in ICU include education, developing guidelines, and ensuring transparency in AI decision-making. A coordinated strategy is essential to balance AI's benefits with the ethical responsibility to protect patients and healthcare providers in critical care settings.

摘要

在澳大利亚的重症监护病房(ICU)中,人工智能(AI)有望提高效率并改善患者预后。然而,在广泛采用之前,必须解决围绕AI的伦理问题。我们使用生物医学伦理四大支柱——行善、不伤害、自主和公正的框架来审视AI的伦理挑战,并讨论设立可解释性这第五大支柱的必要性。我们考虑了延续不平等、侵犯隐私和意外伤害的风险,尤其是在原住民等弱势群体中。我们主张制定一项针对ICU的国家战略,以指导AI的伦理实施,使其与现有的国家AI框架保持一致。我们关于在ICU中实施安全且符合伦理的AI的建议包括教育、制定指南以及确保AI决策的透明度。在重症监护环境中,协调一致的战略对于平衡AI的益处与保护患者和医疗服务提供者的伦理责任至关重要。

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本文引用的文献

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Real-World evaluation of an AI triaging system for chest X-rays: A prospective clinical study.人工智能胸部X光片分诊系统的真实世界评估:一项前瞻性临床研究。
Eur J Radiol. 2024 Dec;181:111783. doi: 10.1016/j.ejrad.2024.111783. Epub 2024 Oct 10.
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Indigenous data sovereignty-A new take on an old theme.本土数据主权——旧主题的新视角。
Science. 2023 Dec;382(6674):eadl4664. doi: 10.1126/science.adl4664. Epub 2023 Dec 1.
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Clinical Informatics needs to be a competency for Intensive care training.临床信息学应成为重症监护培训的一项技能要求。
Crit Care Resusc. 2023 May 20;25(1):6-8. doi: 10.1016/j.ccrj.2023.04.003. eCollection 2023 Mar.
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External validation of AI models in health should be replaced with recurring local validation.健康领域人工智能模型的外部验证应由定期的本地验证取而代之。
Nat Med. 2023 Nov;29(11):2686-2687. doi: 10.1038/s41591-023-02540-z.
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Western Australian medical students' attitudes towards artificial intelligence in healthcare.西澳大利亚州医学生对医疗保健领域人工智能的态度。
PLoS One. 2023 Aug 31;18(8):e0290642. doi: 10.1371/journal.pone.0290642. eCollection 2023.
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Defending explicability as a principle for the ethics of artificial intelligence in medicine.捍卫可解释性原则:人工智能在医学伦理中的应用
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Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality.使用可解释人工智能交叉验证新冠疫情患者死亡率中的社会经济差异。
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Explainable Artificial Intelligence and Cardiac Imaging: Toward More Interpretable Models.可解释人工智能与心脏成像:迈向更具解释力的模型
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Artificial intelligence and the doctor-patient relationship expanding the paradigm of shared decision making.人工智能与医患关系:拓展共同决策模式。
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