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Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things

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  • Frederico M. Bublitz

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    Center for Strategic Technologies in Health (NUTES), State University of Paraiba (UEPB), Campina Grande, PB 58429-500, Brazil
    These authors contributed equally to this work.)

  • Arlene Oetomo

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    These authors contributed equally to this work.)

  • Kirti S. Sahu

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    These authors contributed equally to this work.)

  • Amethyst Kuang

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    These authors contributed equally to this work.)

  • Laura X. Fadrique

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    These authors contributed equally to this work.)

  • Pedro E. Velmovitsky

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    These authors contributed equally to this work.)

  • Raphael M. Nobrega

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    These authors contributed equally to this work.)

  • Plinio P. Morita

    (School of Public Health and Health Systems, University of Waterloo, Waterloo, ON N2L 3G1, Canada
    Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON M5T 3M6, Canada
    Research Institute for Aging, University of Waterloo, Waterloo, ON N2J 0E2, Canada
    Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

Abstract

The purpose of this descriptive research paper is to initiate discussions on the use of innovative technologies and their potential to support the research and development of pan-Canadian monitoring and surveillance activities associated with environmental impacts on health and within the health system. Its primary aim is to provide a review of disruptive technologies and their current uses in the environment and in healthcare. Drawing on extensive experience in population-level surveillance through the use of technology, knowledge from prior projects in the field, and conducting a review of the technologies, this paper is meant to serve as the initial steps toward a better understanding of the research area. In doing so, we hope to be able to better assess which technologies might best be leveraged to advance this unique intersection of health and environment. This paper first outlines the current use of technologies at the intersection of public health and the environment, in particular, Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT). The paper provides a description for each of these technologies, along with a summary of their current applications, and a description of the challenges one might face with adopting them. Thereafter, a high-level reference architecture, that addresses the challenges of the described technologies and could potentially be incorporated into the pan-Canadian surveillance system, is conceived and presented.

Suggested Citation

  • Frederico M. Bublitz & Arlene Oetomo & Kirti S. Sahu & Amethyst Kuang & Laura X. Fadrique & Pedro E. Velmovitsky & Raphael M. Nobrega & Plinio P. Morita, 2019. "Disruptive Technologies for Environment and Health Research: An Overview of Artificial Intelligence, Blockchain, and Internet of Things," IJERPH, MDPI, vol. 16(20), pages 1-24, October.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:20:p:3847-:d:275432
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    References listed on IDEAS

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    1. M. Hino & E. Benami & N. Brooks, 2018. "Machine learning for environmental monitoring," Nature Sustainability, Nature, vol. 1(10), pages 583-588, October.
    2. Donghyun Lee & Suna Kang & Jungwoo Shin, 2017. "Using Deep Learning Techniques to Forecast Environmental Consumption Level," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    3. Shripad Tuljapurkar & Nan Li & Carl Boe, 2000. "A universal pattern of mortality decline in the G7 countries," Nature, Nature, vol. 405(6788), pages 789-792, June.
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    Cited by:

    1. Baoshi Sun & Qiaoli Zhang & Shi Cao, 2020. "Development and Implementation of a Self-Optimizable Smart Lighting System Based on Learning Context in Classroom," IJERPH, MDPI, vol. 17(4), pages 1-26, February.
    2. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Idiano D’Adamo & Assunta Di Vaio & Alessandro Formiconi & Antonio Soldano, 2022. "European IoT Use in Homes: Opportunity or Threat to Households?," IJERPH, MDPI, vol. 19(21), pages 1-18, November.

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