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

Shilpa Srivastava (), Millie Pant and Ritu Agarwal
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Shilpa Srivastava: Noida Institute of Engineering and Technology
Millie Pant: IIT Roorkee
Ritu Agarwal: RKGIT

International Journal of System Assurance Engineering and Management, 2020, vol. 11, issue 2, No 10, 350-365

Abstract: 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.

Keywords: Fuzzy logic; Artificial neural networks; Fuzzy neural; Genetic algorithm; Particle swarm optimization; Deep learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s13198-019-00863-0

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