IDEAS home Printed from https://ideas.repec.org/a/epw/ejece0/v8y2024i3id19617.html

A Comprehensive Artificial Intelligence-Driven Healthcare System

Author

Listed:
  • Frank Edughom Ekpar

    (Topfaith University, Nigeria)

Abstract

The World Health Organization (WHO) states that millions of people worldwide suffer from severe health conditions like diabetes, cardiovascular diseases, stroke, autism, and epilepsy. Some of these conditions, like diabetes, have been on the rise in low-and middle-income countries (LMICs) recently. These conditions have a significant impact on mortality, disability, economic losses, and physical and emotional suffering. However, with more accurate diagnosis, early detection, and prediction of occurrence, these conditions can be treated and managed more effectively, and in some cases, even prevented. This paper presents a comprehensive healthcare system that utilizes artificial intelligence (AI), including large language models (LLMs)–such as Bard and GPT-4 (and their improved future variants), deep learning neural networks, and machine learning platforms such as TensorFlow, electronic health records (EHR), as well as conventional and innovative three-dimensional multilayer EEG systems. The system permits the incorporation of genetic, lifestyle, and environmental information that provides more accurate representations of the participant’s environment and leads to improved health outcomes. This will provide actionable insights for clinical decision support in the early detection, diagnosis, treatment, management, prediction, and prevention of various conditions, including diabetes, cardiovascular diseases, stroke, autism, and epilepsy-saving lives and improving living conditions by reducing the economic, social, psychological and physical burden of the conditions so predicted and possibly prevented, detected early, diagnosed, treated and managed more efficiently. Additionally, the system aims to facilitate practical human-machine interfaces (HMIs) such as brain computer interfaces (BCIs) and progress towards computer-mediated brain-to-brain communication. It also seeks to enhance our understanding of the human brain’s functioning in both normal and diseased states, which can be used for the rehabilitation of individuals with neurological conditions and to create innovative ways for healthy individuals to interact with their environment and improve their lives.

Suggested Citation

  • Frank Edughom Ekpar, 2024. "A Comprehensive Artificial Intelligence-Driven Healthcare System," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 8(3), pages 1-6, May.
  • Handle: RePEc:epw:ejece0:v:8:y:2024:i:3:id:19617
    DOI: 10.24018/ejece.2024.8.3.617
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejece/article/view/19617
    File Function: Abstract page
    Download Restriction: no

    File URL: https://eu-opensci.org/index.php/ejece/article/download/19617/11475
    File Function: Full text
    Download Restriction: no

    File URL: https://libkey.io/10.24018/ejece.2024.8.3.617?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:epw:ejece0:v:8:y:2024:i:3:id:19617. 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: support (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejece .

    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.