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Applications of Artificial Intelligence in COVID-19 ICU and ER situations

Applications of Artificial Intelligence in COVID-19 ICU and ER situations:

  • Because of Covid-19 ill so many people have been affected and this leads to urgent requirement of Intensive care unit (ICU) and Emergency departments (ED) worldwide.
  •  In the regions where coronavirus pandemic hits badly made an immediate need for treatments and novel approaches to deal with the issues.
  •  And one of the novel approaches is the Artificial Intelligence (AI) application. 
  • So the scientists systematically reviewed and assessed the value of current evidence on applications of Artificial Intelligence (AI) for Covid-19 in ICU and ED. 
  • Artificial Intelligence is nothing but the replication of human intelligence by computational method.
  •  Two departments of Artificial Intelligence are deep learning and machine learning which will be working on automatic development of computer programs by training and testing algorithms. 
  • To develop medical AI-based application scientists will use regression models such as cox and logistic regression. 
  • These regression algorithms are the simplest form of Machine learning.
  •  In medical research neural networks, random forest models and support vector machines are becoming more popular. 
  • Artificial Intelligence can help by monitoring the patients in ICU. 
  • The issues concerning the quality of prediction models of Covid-19 developed for disease’s diagnosis and prognosis has been revealed by systematic reviews. 
  • There are some limitations in machine learning studies, insufficient prediction validation and inadequate sample size. 
  • Currently, not much information is available about Artificial Intelligence’s role on decisive technology. 
  • By a thorough review of literature there are eleven predictive AI-based diagnosis models, some studies showed the development of lung segmentation software based on deep learning and the remaining studies is about optimization in the ICU. 
  • Some of the common drawbacks collected by these studies are weak validation, missing data, small sample size. 
  • A small sample size leads to the risk of model optimism and over fitting. 
  • The systematic reviews show that there is a shortage in application of Artificial Intelligence for clinical purposes. 
  • The development and deployment of AI- based applications could help to prevent the current situation.  
  • Integration of new AI-specific reporting guidelines such as SPIRIT-AI and CONSORT-AI into research would help develop novel AI-based applications.

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