IDEAS home Printed from https://ideas.repec.org/a/spr/ijsaem/v11y2020i2d10.1007_s13198-019-00863-0.html
   My bibliography  Save this article

Role of AI techniques and deep learning in analyzing the critical health conditions

Author

Listed:
  • Shilpa Srivastava

    (Noida Institute of Engineering and Technology)

  • Millie Pant

    (IIT Roorkee)

  • Ritu Agarwal

    (RKGIT)

Abstract

The role of a healthcare practitioner is to diagnose a disease and find an optimum means for suitable treatment. This has been the most challenging task over the years. The researchers have been developing intelligent tools for providing support in taking medical decision. The application of AI in different health scenario strengthen the mechanism for finding a better treatment plan. The authors share some recent advancements in this domain. The role of artificial intelligence in Indian healthcare system has also been discussed. The paper analyzes the use of different AI techniques like fuzzy logic, Artificial Neural Networks, Particle Swarm Optimization and Fuzzy Neural in critical health scenario. A detail literature review has been provided in this context. The disease taken into consideration are cancer, TB, diabetes, malaria, orthopedics, obesity and disease specific to elderly people. The purpose of this article is to find the relevance of various techniques of AI in different critical health scenarios. A comparative analysis is done based on the publications since 1995. The challenges and risks associated with the usage of AI in healthcare has been analysed and suggestions made for making the analysis in the health domain more accurate and effective. Further the concept of deep learning has also been explained and its inculcation with the medical domain is discussed.

Suggested Citation

  • Shilpa Srivastava & Millie Pant & Ritu Agarwal, 2020. "Role of AI techniques and deep learning in analyzing the critical health conditions," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 350-365, April.
  • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-019-00863-0
    DOI: 10.1007/s13198-019-00863-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13198-019-00863-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13198-019-00863-0?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Debadri Dutta & Akshit Pradhan & O. P. Acharya & S. K. Mohapatra, 2019. "IoT based pollution monitoring and health correlation: a case study on smart city," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 731-738, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. João Reis & Paula Santo & Nuno Melão, 2020. "Artificial Intelligence Research and Its Contributions to the European Union’s Political Governance: Comparative Study between Member States," Social Sciences, MDPI, vol. 9(11), pages 1-17, November.
    2. Jianfeng Li & Yunfeng Zhang, 2022. "Construction of smart medical assurance system based on virtual reality and GANs image recognition," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2517-2530, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Taizhi Lv & Jun Zhang & Juan Zhang & Yong Chen, 2022. "A path planning algorithm for mobile robot based on edge-cloud collaborative computing," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 594-604, March.

    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:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-019-00863-0. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.