Table 2.

Study Aims, Purposes, Highlights, and Key Results of the Included Studies.

Author	Title	Purposes/aim	Highlights/key results
Li et al15	Artificial Intelligence Distinguishes COVID-19 from Community-Acquired Pneumonia on Chest CT	To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performances	Deep learning detects COVID-19 and uses chest CT to differentiate between lung infection (Pneumonia) and other lung pathologies
Ting et al16	Digital technology and COVID-19	To explore the potential application of 4 inter-related digital technologies (the IoT, big-data analytics, AI, and blockchain) to augmenting 2 traditional public-health strategies for tackling COVID-19: monitoring, surveillance, detection, and prevention of COVID-19; and mitigation of the impact to healthcare indirectly related to COVID-19	A broad range of digital technology is available to augment and enhance public-health strategies for COVID -19 detection, diagnosis, and its healthcare impact.
			The Use of digital technology for managing this global COVID-19 pandemic, a threat to public health, will influence stakeholders (governmental bodies and public) and hence may potentiate future use of such technologies in areas of healthcare other than Infectious diseases like many non-communicable and chronic diseases.
Vaishya et al17	Artificial Intelligence (AI) applications for COVID-19 pandemic	To review the role of AI as a decisive technology to analyze, prepare us for prevention, and fight with COVID-19 and other pandemics.	AI may have an instrumental and crucial role in formulating and developing the COVID-19 vaccine
			AI-based technology can be used for proper detection, analysis, tracing, and tracking of COVID-19 patients, and can predict the disease a-priori among those who may become infected in future
Santosh7	AI-Driven Tools for Coronavirus Outbreak: Need for Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data	Unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employ multitudinal and multimodal data, which is the primary purpose of the paper.	Implementing AI-driven tools at the start of the collection of data enables AI-based models to learn actively and simultaneously in the presence of field experts
			To gain higher confidence during the decision-making process, multiple l data types can be implemented at a time rather than relying just on 1 data type
Shi et al18	Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19	This review aims to extensively discuss the role of medical imaging, especially empowered by AI, in fighting the COVID-19, which will inspire future practical applications and methodological research.	Reliable and efficient imaging solutions are provided by AI for COVID-19
			AI-empowered imaging applications for COVID-19 have been reviewed in detail and their effectiveness is portrayed by 2 imaging modalities, that is, X-ray and CT.
Mashamba-Thompson et al19	Blockchain and Artificial Intelligence Technology for Novel Coronavirus Disease 2019 Self-Testing	A low-cost blockchain and artificial intelligence-coupled self-testing and tracking systems for COVID-19 and other emerging infectious diseases is recommended.	In low resource settings, with poor access to laboratory infrastructure, prompt deployment, and appropriate implementation of a low-cost Blockchain and AI-driven self-testing and tracking systems for COVID-19 can potentially help curb the transmissions of COVID-19, hence lowering the rates of related mortalities.
Allam et al20	On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management	This perspective paper surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same	This paper highlights the urgent need to work towards the standardization of protocols for enhanced smart city communication and the need to democratize the smart city technology sphere to encourage equity and transparency amongst stakeholders, thereby providing more possible cooperation in the case of disasters.
Rao et al21	Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine	The study proposes the use of a machine-learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone-based web survey	AI and deep learning-driven applications if, used promptly, can prove to not only be cost-effective but also be useful in assisting with diagnoses, treatment, decision making, and controlling COVID-19 in populations under quarantine.
Yang et al10	Modified SEIR and AI prediction of the trend of the epidemic of COVID-19 in China under public health interventions	A modified Susceptible Exposed-Infected-Removed (SEIR) epidemiological model that incorporates the domestic migration data before and after January 23 and the most recent COVID-19 epidemiological data to predict the epidemic progression	The Susceptible Exposed-Infected-Recovered (SEIR) model is effective in predicting the COVID-19 epidemic size and peaks
			The AI-based model trained on past SARS dataset shows promising results for predicting future epidemics
Husnayain et al22	Applications of Google Search Trends for risk communication in infectious disease management: A case study of the COVID-19 outbreak in Taiwan	This study explored the potential use of Google Trends (GT) for monitoring public restlessness toward COVID-19 infections in Taiwan	In response to the ongoing pandemic, Google Trends (GT) showed the potential to define the proper timing and location to imply appropriate risk communication strategies for affected populations.
Ayyoubzadeh et al23	Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study	This study aimed to predict the incidence of COVID-19 in Iran	Outbreak trends can be predicted via data mining algorithms, that can aid policymakers and health care managers in appropriate planning and allocation of healthcare resources.
Alimadadi et al24	Artificial intelligence and machine learning to fight COVID-19	N.A.	For artificial intelligence and machine learning research to develop strategies for COVID-19 prediction, diagnosis, and treatment, and for similar pandemics, in the future, a centralized collection of worldwide COVID-19 patient data will be beneficial.
McCall25	COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread		AI can be used to predict future COVID 19 outbreaks and the effect of seasonality on these outbreaks.
			AI is designed to rapidly detect and differentiate between lesions of possible coronavirus pneumonia from other respiratory infections, on images, and their changing lung patterns to provide a quantitative report to assist doctors in quick decision making with accuracy.
			AI applications reduce the burden on clinicians by aiding in the diagnoses and monitoring of COVID-19