公共利益的个人数据:技术增强的抗菌素耐药性监测社会许可的监管决定因素
David J Carter1, Mitchell K Byrne2, Steven P Djordjevic3
1PhD, Faculty of Law, University of Technology Sydney.
Journal of law and medicine
|June 5, 2023
概括
澳大利亚公众支持人工智能 (AI) 和机器学习用于抗菌素耐药性 (AMR) 监测. 公众的信任需要无标识数据,强有力的治理和可靠的AI分析来做出医疗保健决策.
科学领域:
- 一个健康监测
- 抗微生物耐药性 (AMR) 是一种
- 人工智能 (AI) 和机器学习 (ML)
背景情况:
- 技术增强的监测系统正在出现,用于监测人类,动物和环境部门的抗菌素耐药性 (AMR).
- COVID-19大流行加速了类似技术的发展,突出了它们对公共卫生的潜力.
- 在One Health AMR监测中AI/ML的应用处于早期阶段.
研究的目的:
- 探索澳大利亚公众对AI/ML增强的One Health AMR监测的伦理,法律和社会影响的担忧.
- 衡量公众的接受度,并确定实施此类系统的条件.
主要方法:
- 使用了定性研究方法.
- 该研究的重点是确定公众的主要担忧和期望.
主要成果:
- 初步证据表明,公众支持AI/ML增强的One Health AMR监测.
- 公共支持取决于具体的保障措施的实施.
结论:
- 在AMR监测中AI/ML的公众接受取决于强大的数据保护和治理.
- 个人健康数据的非识别,数据使用的严格监管以及可靠的AI驱动分析对于建立公众信任和实现有效的医疗保健决策至关重要.
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