Martinez-Martin Nicole, Luo Zelun, Kaushal Amit, Adeli Ehsan, Haque Albert, Kelly Sara S, Wieten Sarah, Cho Mildred K, Magnus David, Fei-Fei Li, Schulman Kevin, Milstein Arnold
Center for Biomedical Ethics, Stanford University, Stanford, CA, USA.
Department of Computer Science, Stanford University, Stanford, CA, USA.
Lancet Digit Health. 2021 Feb;3(2):e115-e123. doi: 10.1016/S2589-7500(20)30275-2. Epub 2020 Dec 21.
Ambient intelligence is increasingly finding applications in health-care settings, such as helping to ensure clinician and patient safety by monitoring staff compliance with clinical best practices or relieving staff of burdensome documentation tasks. Ambient intelligence involves using contactless sensors and contact-based wearable devices embedded in health-care settings to collect data (eg, imaging data of physical spaces, audio data, or body temperature), coupled with machine learning algorithms to efficiently and effectively interpret these data. Despite the promise of ambient intelligence to improve quality of care, the continuous collection of large amounts of sensor data in health-care settings presents ethical challenges, particularly in terms of privacy, data management, bias and fairness, and informed consent. Navigating these ethical issues is crucial not only for the success of individual uses, but for acceptance of the field as a whole.
环境智能在医疗保健领域的应用越来越广泛,例如通过监测工作人员对临床最佳实践的遵守情况来帮助确保临床医生和患者的安全,或者减轻工作人员繁琐的文档记录任务。环境智能涉及在医疗保健环境中使用非接触式传感器和基于接触的可穿戴设备来收集数据(例如物理空间的成像数据、音频数据或体温),并结合机器学习算法来高效且有效地解释这些数据。尽管环境智能有望提高医疗质量,但在医疗保健环境中持续收集大量传感器数据带来了伦理挑战,特别是在隐私、数据管理、偏差与公平以及知情同意方面。应对这些伦理问题不仅对个别应用的成功至关重要,对整个领域的接受度也至关重要。