**5. Conclusion**

The COVID-19 is a disease that has spread all over the world. This work attempted to provide a detailed study on how the AI and ML can help in various domains related to COVID-19, specifically in the area of disease diagnosis using CT imagery. In pursuing so, we considered, examined, discussed, and analyzed comprehensive studies and detailed researches proposed by intellectuals and researchers from various scientific communities and international academic institutions. Deep learning techniques and algorithms have shown immense appearances and implementations in different domains and COVID-19 related applications.

AI solutions have the potential to detect and analyze any abnormalities in health conditions in general, and related to COVID-19 in particular. The study has demonstrated that AI solutions can assist in differentiating Coronavirus patients from those who do not have the disease and can provide support in tracking disease progression. AI technology can potentially support radiologists in the triage, quantification, and trend analysis of data. For example, if the developed technique suggests a significantly increased likelihood of disease, then the case can be flagged for further review by a radiologist or clinician for possible treatment/quarantine. Moreover, AI technology can provide a consistent method for rapid evaluation of high volumes of diagnostic that can reliably exclude images which are negative for findings associated with COVID-19. This decreases the volume of cases passing through to the radiologist without overlooking positive cases. Using AI solutions, progression and regression of positive findings could be monitored more quantitatively and regularly. This could lead to more effective identification and containment of early cases. The study also discovered that a critical existing impediment to effective AI implementation is the lack of COVID-19-related clinical data that can be maintained and processed into easily accessible databases. Integrating COVID-19-related clinical data with existing biobanks, as well as pre-existing patient data, could help bioinformaticians and computational scientists develop a faster and more practical way to useful data-mining.

It is our hypothesis that AI and ML tools can leverage the ability to modify and adapt existing models and combine them with initial clinical understanding to address COVID-19 challenges and the new emerging strains or mutations of the virus. Researchers and scientists are hoping to speed up the development of extremely precise and useful AI, ML, and deep learning technologies to combat COVID-19. If our societies could not reach the best expected AI solutions during this pandemic, we strongly anticipate that AI technology will be of greater help with the next pandemic.

#### **Acknowledgements**

The author would like to express his gratitude and grateful appreciation to the Kuwait Foundation for the Advancement of Sciences (KFAS) for financially supporting this project. The project was fully funded by KFAS under project code: PN20-13NH-03.

*AI Modeling to Combat COVID-19 Using CT Scan Imaging Algorithms and Simulations: A Study DOI: http://dx.doi.org/10.5772/intechopen.99442*
