IDEAS home Printed from https://ideas.repec.org/a/alk/jcsts0/v7y2025i4id9442.html

Real-Time AI-Powered Predictive Analytics in Cloud-Based Healthcare Platforms: From Concept to Implementation

Author

Listed:
  • Venkateswara Reddi Cheruku

    (SVIT Inc, USA)

Abstract

Real-time artificial intelligence predictive analytics systems in cloud-based healthcare environments are comprehensively explored in this article. It examines the technical architecture, implementation challenges, and clinical outcomes of systems designed for early detection of critical conditions such as sepsis and acute cardiac events. The integration of streaming data processing, machine learning algorithms, and cloud infrastructure creates powerful tools that can significantly reduce mortality and morbidity through timely interventions. The article delves into architectural frameworks, data pipeline engineering, model selection considerations, inference optimization, clinical workflow integration, performance validation protocols, regulatory compliance requirements, and emerging trends in the field. Healthcare technology professionals will find essential insights for successful implementation strategies, addressing common obstacles, and understanding future development directions for predictive healthcare systems.

Suggested Citation

  • Venkateswara Reddi Cheruku, 2025. "Real-Time AI-Powered Predictive Analytics in Cloud-Based Healthcare Platforms: From Concept to Implementation," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 7(4), pages 21-32, May.
    • Handle: RePEc:alk:jcsts0:v:7:y:2025:i:4:id:9442
    DOI: 10.32996/jcsts.2025.7.4.3
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jcsts/article/view/9442
    File Function: Abstract page
    Download Restriction: no
    File URL: https://al-kindipublisher.com/index.php/jcsts/article/download/9442/8099
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32996/jcsts.2025.7.4.3?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:jcsts0:v:7:y:2025:i:4:id:9442. 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/jcsts .

    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.