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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
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    References listed on IDEAS

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    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.
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    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.

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