Abstract
Background and aims
Artificial Intelligence (AI) shows extensive capabilities to impact different healthcare areas during the COVID-19 pandemic positively. This paper tries to assess the capabilities of AI in the field of cardiology during the COVID-19 pandemic. This technology is useful to provide advanced technology-based treatment in cardiology as it can help analyse and measure the functioning of the human heart.
Methods
We have studied a good number of research papers on Artificial Intelligence on cardiology during the COVID-19 pandemic to identify its significant benefits, applications, and future scope. AI uses artificial neuronal networks (ANN) to predict. In cardiology, it is used to predict the survival of a COVID-19 patient from heart failure.
Results
AI involves complex algorithms for predicting somewhat successful diagnosis and treatments. This technology uses different techniques, such as cognitive computing, deep learning, and machine learning. It is incorporated to make a decision and resolve complex challenges. It can focus on a large number of diseases, their causes, interactions, and prevention during the COVID-19 pandemic. This paper introduces AI-based care and studies its need in the field of cardiology. Finally, eleven major applications of AI in cardiology during the COVID-19 pandemic are identified and discussed.
Conclusions
Cardiovascular diseases are one of the major causes of death in human beings, and it is increasing for the last few years. Cardiology patients' treatment is expensive, so this technology is introduced to provide a new pathway and visualise cardiac anomalies. AI is used to identify novel drug therapies and improve the efficiency of a physician. It is precise to predict the outcome of the COVID-19 patient from cardiac-based algorithms. Artificial Intelligence is becoming a popular feature of various engineering and healthcare sectors, is thought for providing a sustainable treatment platform. During the COVID-19 pandemic, this technology digitally controls some processes of treatments.
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