Researchers and scientists can use computational-based models to turn linked data into useful information, aiding in disease diagnosis, examination, and viral containment due to recent artificial intelligence and machine learning breakthroughs. In this paper, we extensively study the role of artificial intelligence and machine learning in delivering efficient responses to the COVID-19 pandemic almost four years after its start. In this regard, we examine a large number of critical studies conducted by various academic and research communities from multiple disciplines, as well as practical implementations of artificial intelligence algorithms that suggest potential solutions in investigating different COVID-19 decision-making scenarios. We identify numerous areas where artificial intelligence and machine learning can impact this context, including diagnosis (using chest X-ray imaging and CT imaging), severity, tracking, treatment, and the drug industry. Furthermore, we analyse the dilemma's limits, restrictions, and hazards.
Keyphrases
- artificial intelligence
- machine learning
- big data
- sars cov
- deep learning
- coronavirus disease
- high resolution
- decision making
- climate change
- computed tomography
- dual energy
- healthcare
- multidrug resistant
- magnetic resonance
- risk assessment
- living cells
- image quality
- health information
- fluorescence imaging
- positron emission tomography
- quantum dots
- combination therapy
- replacement therapy
- electron microscopy