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Artificial Intelligence and Big Data in Public Health

Author

Listed:
  • Kurt Benke

    (School of Engineering, University of Melbourne, Parkville, Victoria 3010, Australia
    AgriBio, Centre for AgriBiosciences, State Government of Victoria, Bundoora, Victoria 3083, Australia)

  • Geza Benke

    (School of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd, Melbourne 3004, Australia)

Abstract

Artificial intelligence and automation are topics dominating global discussions on the future of professional employment, societal change, and economic performance. In this paper, we describe fundamental concepts underlying AI and Big Data and their significance to public health. We highlight issues involved and describe the potential impacts and challenges to medical professionals and diagnosticians. The possible benefits of advanced data analytics and machine learning are described in the context of recently reported research. Problems are identified and discussed with respect to ethical issues and the future roles of professionals and specialists in the age of artificial intelligence.

Suggested Citation

  • Kurt Benke & Geza Benke, 2018. "Artificial Intelligence and Big Data in Public Health," IJERPH, MDPI, vol. 15(12), pages 1-9, December.
    • Handle: RePEc:gam:jijerp:v:15:y:2018:i:12:p:2796-:d:189232
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    References listed on IDEAS

    as
    1. Christopher Brzozek & Kurt K. Benke & Berihun M. Zeleke & Michael J. Abramson & Geza Benke, 2018. "Radiofrequency Electromagnetic Radiation and Memory Performance: Sources of Uncertainty in Epidemiological Cohort Studies," IJERPH, MDPI, vol. 15(4), pages 1-19, March.
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    Cited by:

    1. Andrea Spini & Giulia Hyeraci & Claudia Bartolini & Sandra Donnini & Pietro Rosellini & Rosa Gini & Marina Ziche & Francesco Salvo & Giuseppe Roberto, 2021. "Real-World Utilization of Target- and Immunotherapies for Lung Cancer: A Scoping Review of Studies Based on Routinely Collected Electronic Healthcare Data," IJERPH, MDPI, vol. 18(14), pages 1-21, July.
    2. Wen-Yu Ou Yang & Cheng-Chien Lai & Meng-Ting Tsou & Lee-Ching Hwang, 2021. "Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data," IJERPH, MDPI, vol. 18(14), pages 1-12, July.
    3. Heather Behr & Annabell Suh Ho & Ellen Siobhan Mitchell & Qiuchen Yang & Laura DeLuca & Andreas Michealides, 2021. "How Do Emotions during Goal Pursuit in Weight Change over Time? Retrospective Computational Text Analysis of Goal Setting and Striving Conversations with a Coach during a Mobile Weight Loss Program," IJERPH, MDPI, vol. 18(12), pages 1-15, June.
    4. Daniele Piovani & Stefanos Bonovas, 2022. "Real World—Big Data Analytics in Healthcare," IJERPH, MDPI, vol. 19(18), pages 1-3, September.
    5. Claus Zippel & Sabine Bohnet-Joschko, 2021. "Rise of Clinical Studies in the Field of Machine Learning: A Review of Data Registered in ClinicalTrials.gov," IJERPH, MDPI, vol. 18(10), pages 1-14, May.
    6. Julien Issa & Raphael Olszewski & Marta Dyszkiewicz-Konwińska, 2022. "The Effectiveness of Semi-Automated and Fully Automatic Segmentation for Inferior Alveolar Canal Localization on CBCT Scans: A Systematic Review," IJERPH, MDPI, vol. 19(1), pages 1-10, January.

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