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Illuminating the dark spaces of healthcare with ambient intelligence

Author

Listed:
  • Albert Haque

    (Stanford University)

  • Arnold Milstein

    (Stanford University School of Medicine)

  • Li Fei-Fei

    (Stanford University
    Stanford University)

Abstract

Advances in machine learning and contactless sensors have given rise to ambient intelligence—physical spaces that are sensitive and responsive to the presence of humans. Here we review how this technology could improve our understanding of the metaphorically dark, unobserved spaces of healthcare. In hospital spaces, early applications could soon enable more efficient clinical workflows and improved patient safety in intensive care units and operating rooms. In daily living spaces, ambient intelligence could prolong the independence of older individuals and improve the management of individuals with a chronic disease by understanding everyday behaviour. Similar to other technologies, transformation into clinical applications at scale must overcome challenges such as rigorous clinical validation, appropriate data privacy and model transparency. Thoughtful use of this technology would enable us to understand the complex interplay between the physical environment and health-critical human behaviours.

Suggested Citation

  • Albert Haque & Arnold Milstein & Li Fei-Fei, 2020. "Illuminating the dark spaces of healthcare with ambient intelligence," Nature, Nature, vol. 585(7824), pages 193-202, September.
    • Handle: RePEc:nat:nature:v:585:y:2020:i:7824:d:10.1038_s41586-020-2669-y
    DOI: 10.1038/s41586-020-2669-y
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    Cited by:

    1. Mohamed-Amine Choukou & Sophia Mbabaali & Ryan East, 2021. "Healthcare Professionals’ Perspective on Implementing a Detector of Behavioural Disturbances in Long-Term Care Homes," IJERPH, MDPI, vol. 18(5), pages 1-16, March.
    2. Tinglong Dai & Sridhar Tayur, 2022. "Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4443-4451, December.
    3. Ali Sunyaev & Daniel Fürstenau & Elizabeth Davidson, 2024. "Reimagining Digital Health," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(3), pages 249-260, June.
    4. Han, Yilong & Li, Yinbo & Li, Yongkui & Yang, Bin & Cao, Lingyan, 2023. "Digital twinning for smart hospital operations: Framework and proof of concept," Technology in Society, Elsevier, vol. 74(C).
    5. Stahl, Bernd Carsten & Brooks, Laurence & Hatzakis, Tally & Santiago, Nicole & Wright, David, 2023. "Exploring ethics and human rights in artificial intelligence – A Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    6. Ioana Andreea Bogoslov & Sorina Corman & Anca Elena Lungu, 2024. "Perspectives on Artificial Intelligence Adoption for European Union Elderly in the Context of Digital Skills Development," Sustainability, MDPI, vol. 16(11), pages 1-34, May.

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