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.

中文翻译：

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人工智能技术和深度学习在分析关键健康状况中的作用

保健医生的作用是诊断疾病并找到合适治疗的最佳方法。多年来，这一直是最具挑战性的任务。研究人员一直在开发智能工具，为医疗决策提供支持。AI在不同健康状况下的应用加强了寻找更好治疗方案的机制。作者分享了该领域的一些最新进展。还讨论了人工智能在印度医疗系统中的作用。本文分析了关键健康场景中不同AI技术的使用，例如模糊逻辑，人工神经网络，粒子群优化和模糊神经。在这种情况下提供了详细的文献综述。考虑的疾病包括癌症，结核病，糖尿病，疟疾，骨科，肥胖和特定于老年人的疾病。本文的目的是发现各种AI技术在不同的关键健康场景中的相关性。根据1995年以来的出版物进行了比较分析。已经分析了与AI在医疗保健中使用相关的挑战和风险，并提出了一些建议，以使在健康领域的分析更加准确和有效。此外，还解释了深度学习的概念，并讨论了其与医学领域的融合。分析了与AI在医疗保健中使用相关的挑战和风险，并提出了一些建议，以使在医疗领域的分析更加准确和有效。此外，还解释了深度学习的概念，并讨论了其与医学领域的融合。分析了与AI在医疗保健中使用相关的挑战和风险，并提出了一些建议，以使在医疗领域的分析更加准确和有效。此外，还解释了深度学习的概念，并讨论了其与医学领域的融合。