IDEAS home Printed from https://ideas.repec.org/a/alk/jbms00/v2y2020i2id11064.html

Converging Artificial Intelligence, Big Data, and Biotechnology: A Transformative Framework for Next-Generation Drug Discovery and Global Health Intelligence

Author

Listed:
  • Md Mazharul Anwar

    (Department of Statistics and Data Science, Jahangirnagar University, Savar, Bangladesh)

Abstract

Global health and pharmaceutical innovation are undergoing a radical shift due to the convergence of biotechnology, wearable health devices, big data analytics, and artificial intelligence (AI). This article summarizes four core studies: biotechnology-driven strategy models for global competitiveness, wearables for real-time cardiovascular disease monitoring, AI-based drug discovery, and big data frameworks for AMR surveillance. Together, these studies show how biotechnology frameworks, generative algorithms, and predictive analytics may improve preventative healthcare, accelerate drug discovery, and strengthen global health security. Supported by more than 30 scientific publications, this article offers a comprehensive conceptual framework for next-generation pharmaceutical innovation that integrates biomedical data streams, computational intelligence, and biotechnology market strategies. The findings highlight the need for strategic policymaking, ethical governance, and interdisciplinary cooperation to transform technological breakthroughs into long-term global health solutions.

Suggested Citation

  • Md Mazharul Anwar, 2020. "Converging Artificial Intelligence, Big Data, and Biotechnology: A Transformative Framework for Next-Generation Drug Discovery and Global Health Intelligence," Journal of Business and Management Studies, Al-Kindi Center for Research and Development, vol. 2(2), pages 82-87, October.
    • Handle: RePEc:alk:jbms00:v:2:y:2020:i:2:id:11064
    DOI: 10.32996/jbms.2020.2.2.8
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jbms/article/view/11064
    File Function: Abstract page
    Download Restriction: no
    File URL: https://al-kindipublisher.com/index.php/jbms/article/download/11064/9838
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32996/jbms.2020.2.2.8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:alk:jbms00:v:2:y:2020:i:2:id:11064. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jbms .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.