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
- high resolution
- coronavirus disease
- decision making
- computed tomography
- climate change
- dual energy
- healthcare
- multidrug resistant
- electronic health record
- magnetic resonance
- risk assessment
- image quality
- sensitive detection
- respiratory syndrome coronavirus
- drug induced
- replacement therapy
- combination therapy
- adverse drug
- smoking cessation