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Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects

Author

Listed:
  • Rola Khamisy-Farah

    (Clalit Health Service, Akko, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 13100, Israel)

  • Leonardo B. Furstenau

    (Department of Industrial Engineering, Federal University of Rio Grande do Sul, Porto Alegre 90035-190, Brazil)

  • Jude Dzevela Kong

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

  • Jianhong Wu

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

  • Nicola Luigi Bragazzi

    (Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada)

Abstract

Tremendous scientific and technological achievements have been revolutionizing the current medical era, changing the way in which physicians practice their profession and deliver healthcare provisions. This is due to the convergence of various advancements related to digitalization and the use of information and communication technologies (ICTs)—ranging from the internet of things (IoT) and the internet of medical things (IoMT) to the fields of robotics, virtual and augmented reality, and massively parallel and cloud computing. Further progress has been made in the fields of addictive manufacturing and three-dimensional (3D) printing, sophisticated statistical tools such as big data visualization and analytics (BDVA) and artificial intelligence (AI), the use of mobile and smartphone applications (apps), remote monitoring and wearable sensors, and e-learning, among others. Within this new conceptual framework, big data represents a massive set of data characterized by different properties and features. These can be categorized both from a quantitative and qualitative standpoint, and include data generated from wet-lab and microarrays (molecular big data), databases and registries (clinical/computational big data), imaging techniques (such as radiomics, imaging big data) and web searches (the so-called infodemiology, digital big data). The present review aims to show how big and smart data can revolutionize gynecology by shedding light on female reproductive health, both in terms of physiology and pathophysiology. More specifically, they appear to have potential uses in the field of gynecology to increase its accuracy and precision, stratify patients, provide opportunities for personalized treatment options rather than delivering a package of “one-size-fits-it-all” healthcare management provisions, and enhance its effectiveness at each stage (health promotion, prevention, diagnosis, prognosis, and therapeutics).

Suggested Citation

  • Rola Khamisy-Farah & Leonardo B. Furstenau & Jude Dzevela Kong & Jianhong Wu & Nicola Luigi Bragazzi, 2021. "Gynecology Meets Big Data in the Disruptive Innovation Medical Era: State-of-Art and Future Prospects," IJERPH, MDPI, vol. 18(10), pages 1-13, May.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5058-:d:552045
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    References listed on IDEAS

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    1. Cathryn M. Delude, 2015. "Deep phenotyping: The details of disease," Nature, Nature, vol. 527(7576), pages 14-15, November.
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    Cited by:

    1. Angelos I. Stoumpos & Fotis Kitsios & Michael A. Talias, 2023. "Digital Transformation in Healthcare: Technology Acceptance and Its Applications," IJERPH, MDPI, vol. 20(4), pages 1-44, February.
    2. Rola Khamisy-Farah & Peter Gilbey & Leonardo B. Furstenau & Michele Kremer Sott & Raymond Farah & Maurizio Viviani & Maurizio Bisogni & Jude Dzevela Kong & Rosagemma Ciliberti & Nicola Luigi Bragazzi, 2021. "Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature," IJERPH, MDPI, vol. 18(17), pages 1-16, August.

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