Title	Aims	Strengths	Limitations	Conclusion
Approaches Based on artificial intelligence and the internet of intelligent things to prevent the spread of COVID-19: Scoping review	To analyze the current literature, discuss the applicability of reported ideas for using AI to prevent and control COVID-19, and build a comprehensive view of how current systems may be useful in particular areas (i.e. health care administrators, computer scientists, and policy makers worldwide)	The article describes the multidisciplinary applications of AI and real time efficacy in the mitigation of COVID-19.	Lacks other studies that might provide some additional perspective in the scope of AI and COVID-19.	The field of medical AI applications remains at early stages which was evident in the lack of literature pertaining to medical AI applications (i.e. resource allocation and experimental therapeutics).
Review of big data analytics, artificial intelligence and nature-inspired computing models towards accurate detection of COVID-19 pandemic cases and contact tracing	To compare the characteristics of big data, AI, nature-inspired computing models in terms of accuracy of COVID-19 contact tracing.	The article describes the role of technologies in the mitigation of COVID-19, clearly illustrates the addressed concepts in a simple and applicable language.	Lacks graphic representations through charts and figures that could display the efficacy of the proposed techniques in the mitigation of COVID-19.	AI models can help to address the gap in COVID-19 diagnostics but with a limited capacity that requires further programming and subject compliance.
Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: Taxonomy analysis, challenges, future solutions and methodological aspects	To present AI techniques used in COVID-19 diagnostics with the notions of medical imaging benchmarking and evaluation.	Highlights a clear scope and compares other relevant studies that discuss the addressed topic, provides a proposed solution to address the lack in AI literature, and demonstrates the findings in an assortment of summarization tables that highlight the points of interest.	Lacks the input of experienced radiologists’ and real-world applications of the proposed solution being MCDA to ascertain the platform’s efficacy in addressing the shortcomings of AI.	The diversity of AI technologies can pose a challenge in terms of usage rationale during the suitable situation.
The Rise of Machine Intelligence in the COVID-19 Pandemic and Its Impact on Health Policy. In Surveying the Covid-19 Pandemic and its Implications	To emphasize the need of targeted use of technology to address future pandemics.	Provides clear details regarding the role of AI in the mitigation of COVID-19, highlights the chain of events related to the emergence and spread of COVID-19, and explains the background of AI and digital technologies to help curb COVID-19.	Lacks definitions of AI related concepts and real-time evaluation of the AI techniques.	Successful use of technology can mitigate the risks of future pandemics following COVID-19.
Extracting Possibly Representative COVID-19 Biomarkers from X-ray Images with Deep Learning Approach and Image Data Related to Pulmonary Diseases	Autonomous detection of COVID-19 using transfer learning, deep learning, and CNNs.	The article is critically organized with a detailed purpose, methods and results.	The pneumonia incidence samples are older recorded samples and do not represent pneumonia images from patients with suspected Coronavirus symptoms.	The proposed study method delivered 99.42 % specificity, 99.18 % accuracy, and 97.36 % sensitivity identifying biological markers of COVID-19.
A Study on Fight Against COVID-19 from Latest Technological Intervention	To study the efficacy of technological interventions against COVID-19 in aspects of home quarantine and AI-assisted diagnostics.	Provides a clear overview of the article with a detailed description of the diagnosis and therapeutics.	Lack of clear definition of AI and background or details about COVID-19.	AI offers the prospect of the full functional capability to mitigate COVID-19.
Utility of Artificial Intelligence Amidst the COVID 19 Pandemic: A Review	To describe the history and utility of machine learning in mitigating infectious pandemics and in retrospect to COVID-19.	Employs a consistent progression of ideas starting from the definition of machine learning and ending with machine learning’s clinical utility against COVID-19.	Lacks images and real-time incident involving the application of ML in a COVID-19 related setting.	Machine learning is a flexible AI technology manipulated in various medical circumstances, namely, mitigation of infectious pandemics.
How Big Data and Artificial Intelligence Can Help Against COVID-19	To highlight and summarize the applications of AI and big data in the global efforts against COVID-19.	Explains the potential of AI and big data in the mitigation of COVID-19 using a hierarchal approach that includes short, mid, and long term application.	Lacks real-time applications that might provide additional illustration and understanding of AI and big data implementation in the COVID-19 response.	Big data and artificial intelligence can provide short, mid, and long term applications that may influence COVID-19 and future development concepts.
Digital technologies in the public-health response to COVID-19	To capture the breadth of digital innovations for the public-health response to COVID-19 worldwide and their limitations, and barriers to their implementation, including legal, ethical and privacy barriers, as well as organizational and workforce barriers.	Offers a comprehensive background on COVID-19 and a wide explanation of the epidemiological aspects of COVID-19 using graphs and tables.	No notable limitations were mentioned.	COVID-19 inherently promoted digital technologies and allowed the exploration of the possibility of digitized healthcare.
Can AI Help in Screening Viral and COVID-19 Pneumonia?	Autonomous detection of COVID-19 pneumonia using transfer learning.	The publication displayed an organized outline, abstract, sampling method, and research methodology.	The publication fails to mention the participants’ awareness about the nature of the research.	The transfer learning system managed to train CNNs to achieve readings of 99.7 %, 99.7 %, 99.7 %, 99.55 %,97.9 %, 97.95 %, 97.9 %, and 98.8 % in taxonomical accuracy, pathological sensitivity, and specificity to COVID-19.
Will COVID-19 be the tipping point for the Intelligent Automation of work? A review of the debate and implications for research	To review the arguments in favor and opposition of increasing the level of AI adoption stimulated by COVID-19 with reflection on the influence of this argument in healthcare research.	Clearly explained the key arguments in favor of increased AI adoption include consumer preferences, increasing familiarity with AI technologies, and increased business confidence in AI.	Short conclusion and evident lack of in-text citations.	Information systems and management will become a topic of interest following the recovery after COVID-19.
Machine learning to assist clinical decision-making during the COVID-19 pandemic	To depict the importance and incidence of the emergency ML phenomena to help aid healthcare professional exercises evidence based practice and clinical decision making.	Portrays the multifaceted role of ML/AI in the mitigation of COVID-19 through many clinical scenarios and ethical-legal issues from healthcare professionals and patients alike.	Lacks statistical information on ML/AI’s actual implementation during COVID-19 including healthcare professionals and the perspective of healthcare professionals on ML/AI modals.	Medical machine learning is a prospect raised by COVID-19 and can offer a vast utility of applications to help facilitate the workflow undertaken in clinical healthcare facilities worldwide.
Rapid implementation of mobile technology for real-time epidemiology of COVID-19	The goal was to establish the Coronavirus Pandemic Epidemiology (COPE) group to invite international scientists with expertise in big data research and epidemiology to develop a COVID-19 Symptom Tracker mobile application.	The publication offered full disclosure of the study participants being the Welsh Government, NHS Wales, the Scottish Government, and NHS Scotland. The approach has the benefit of allowing rapid deployment across a large cross-section of the population during an unprecedented health crisis.	Smartphone application does not represent a random sampling of the population.	The proposed approach offers early stages regarding the discussed concepts and novel consortium. The app was first launched in the UK on March 24th 2020, as opposed to March 29th in the USA, in which the app managed to garner more than 2.8 million users as of May 2, 2020.
Coronavirus Disease 2019 (COVID-19) diagnostic technologies: A country-based retrospective analysis of screening and containment procedures during the first wave of the pandemic	To illustrate country-based retrospective analysis of screening and containment procedures during the first wave of the pandemic.	Well organized article with clear details of the presented information.	Screening protocols must consider subspecialist expertise and time to diagnosis, in addition to diagnostic accuracy. National and institutional protocols must consider local availability of resources.	Diagnosis of COVID-19 is challenging due to a prolonged asymptomatic phase. RT-PCR has been considered the gold standard. However, suboptimal sensitivity in early disease and regional shortages of testing kits have limited its use.
Digital Response During the COVID-19 Pandemic in Saudi Arabia	The aim is to highlight how Saudi Arabia has used digital technology during the COVID-19 pandemic in the domains of public health, health care services, education, telecommunication, commerce, and risk communication.	All digital solutions and tools used to encompass during the COVID-19 outbreak in Saudi Arabia up to manuscript revision.	This paper lists apps but does not evaluate them or check for user experiences. Moreover, the criteria for inclusion in this paper were subjective. The authors attempted to decrease the effect of this subjectivity using discussion and consensus.	The Saudi Vision 2030 framework, released in 2017, has paved the path for digital transformation. COVID-19 enabled the promotion and testing of this transition. In Saudi Arabia, artificial intelligence in integrating different data sources during future outbreaks could be further explored. Also, decreasing the number of mobile apps and merging their functions could increase and facilitate their use.
COVID-19: What Can Healthcare Learn?	To demonstrate facts related to COVID-19 and to highlight on the role of healthcare workers during this pandemic.	The article has valuable recommendations related to COVID-19.	Lack of abstract, background, and conclusion.	The safety and protection of healthcare workers should be a top priority. Protective gear should be provided to healthcare workers immediately because, in the end, these are the people who will play an essential role in minimizing the level of illness and the number of deaths.
AI Techniques for COVID-19	The aim of this study is to summarize the current state of AI applications in clinical administrations while battling COVID-19 and also to highlight various intelligence techniques and methods that can be applied to various types of medical information-based pandemic.	There was an attempt to benefit medical practitioners and medical researchers in overpowering their difficulties while handling COVID-19 big data. This survey study has also ended up with a detailed discussion about how AI implementation can be a massive advantage in combating various similar viruses.	Lacks some background information regarding AI (i.e. Definition and history of development).	The artificial intelligence tool is arriving at the clinical field in present times. AI techniques help in speeding up researches and assisting in the current COVID-19 crisis. Computer-based intelligence is not just useful in treating COVID-19 contaminated patients yet also for their proper medical check-ups. It can follow the emergency of COVID-19 at various scales, for example, clinical and epidemiological applications.
Implementation of a deep learning-based computer aided detection system for the interpretation of chest radiographs in patients suspected for covid-19	The aim of this study is ‘to describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance.	The results were clear and well explained in details	Small sample size	Radiologists with CAD assistance could identify patients with PCR-positive COVID-19 or pneumonia on CXR with an acceptable performance. In patients suspected of COVID-19, CXR had much faster TATs than PCRs.
Digital healthcare: The only solution for better healthcare during COVID-19 pandemic?	The paper focuses on ‘how digital solutions can impact healthcare during this pandemic’.	The information is clear and well organized.	Brief abstract and lack of detailed introduction.	Digital health systems are well suited to provide novel solutions to the public health emergency. These include the development of robust surveillance systems, telehealth, novel diagnostic and clinical decision-making tools.
Covid-19 and health care’s digital revolution	To demonstrate the importance of digital technology during COVID-19 and to show some important services that should be considered during this pandemic.	Information is clear and well organized.	Lack of clear abstract and conclusion.	Digital and technological revolution have transformed the world over the past century. As health care systems nationwide brace for a surge of COVID-19 cases, urgent action is required to transform health care delivery and scale up our systems by unleashing digital technologies power.
A Review for Artificial Intelligence Proving to Fight Against COVID-19 Pandemic and Prefatory Health Policy	The aim of the study is to discuss the various aspects of modern technology used to fight against COVID-19 outbreak crisis at different scales, including medical image processing, disease tracking, prediction, outcomes, computational biology and medicines.	The study uses various data sources, including MEDLINE, Global Health, PsycINFO, and Scopus databases.	No notable limitations were mentioned.	Emerging technologies are set to play an essential role in our response to the COVID-19 pandemic. There is an interest for future work on building up a benchmark framework to assess and look at the current techniques. The present models acquired extraordinary accuracy in recognizing COVID-19 symptoms with different kinds of viral pneumonia utilizing radiology imaging.
COVID-19 pneumonia diagnosis using a simple 2d deep learning framework with a single chest CT image: Model development and validation	The study aimed to rapidly develop an AI technique to diagnose COVID-19 pneumonia in CT images and differentiate it from non–COVID-19 pneumonia and non-pneumonia diseases.	The study specifics a precise research method as well as a validation process.	Lack of a COVID-19 CT database that allows additional training of the platform	FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia.
		The paper clearly describes the implications and testing parameters of each measured category under uniform scientific testing conditions.	Small sample size was used
		The findings were illustrated through several means including tables and graphic heat signatures which provided additional convenience and understanding of the paper. The study included a performance comparison between the proposed platform and radiologist.
Artificial intelligence-powered search tools and resources in the fight against covid-19	This paper explores three prominent initiatives: COVID-19 focused datasets (e.g., CORD-19); Artificial intelligence-powered search tools (e.g., WellAI, SciSight); and contact tracing based on mobile communication technology.	The article was organized critically including the tables and diagrams.	No notable limitations were mentioned.	The new AI-powered search tools will accelerate research and development in COVID-19 as the world strives to develop efficient and timely testing and effective therapies to combat this pandemic.
How Might AI and Chest Imaging Help Unravel COVID-19′s Mysteries?	The study aims to explore the role of AI-chest image in detecting COVID-19.	Information was well organized in details.	The Summary and abstract were short and not specific. Lack of conclusion.	Artificial intelligence (AI) can expand the role of chest imaging in COVID-19 beyond diagnosis to enable risk stratification, treatment monitoring, and the discovery of novel therapeutic targets.
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review	The article aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic.	The publication employs summarization tables enabling full comprehension and illustration of the presented information.	The study lacks information about the integration of AI in COVID-19 related medical imaging.	The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and the drug/vaccine development process for COVID-19.
Combat COVID-19 with artificial intelligence and big data.	To demonstrate the role of AI and big data in the mitigation of COVID-19 in Asian countries.	Information was well organized in details.	Minimal information on additional healthcare applications of AI during COVID-19 and lack of definitions of technology associated terminologies.	Researchers and technology companies are exploring ways to improve contact-tracing systems without mass surveillance to achieve the benefits of location-tracking while protecting individual privacy.
Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic	The intention of this study is to uncover the hidden roles of technologies that ultimately help for controlling the pandemic.	Information was well organized in details.	No notable limitations were mentioned.	The strategies utilizing potential technologies would yield better benefits during the pandemic which in turn aids in controlling the spreading of infection.
COVID-19: A Multimodality Review of Radiologic Techniques, Clinical Utility, and Imaging Features	To review the radiological utility of COVID-19 being CT, CXR, US, NM, and echocardiography.	The article provides a clear and detailed investigation of each radiographic modality’s associated characteristics implemented in the screening process of COVID-19.	The study discusses AI with minimal details and lacks definition associated with the discipline of AI.	CXR and CT observations have been concluded to be the most effective diagnostic modalities. US works well with CT. A comprehensive description of MRI and PET is needed.
Technology and its Solutions in the Era of COVID-19 Crisis: A Review of Literature.	The study aims to investigate the technologies that have been applied to solve the COVID-19 crisis.	The review is logically organized and offers a balanced critical analysis of the literature.	Short conclusion	Technologies with the ability to reduce human contacts through teleservices and those that quickly enable decision-making via in-depth analysis received more attention among the health authorities and organizations.
Integrating emerging technologies into COVID-19 contact tracing: Opportunities, challenges and pitfalls.	The paper focuses on contact tracing apps by using GPS, Wi-Fi, Bluetooth, Social graph and Card transaction data to track users as well as AI.	Information was well organized in details.	Technical limitation Dealing with asymptomatic individuals.	Integrating emerging technologies into COVID-19 contact tracing is seen as a viable option in mitigating coronavirus spread.
Imaging of COVID-19 pneumonia: Patterns, pathogenesis, and advances	To highlight common imaging findings using illustrative examples, describe the evolution of disease over time, discuss differences in imaging appearance of adult and pediatric patients and review the available literature on quantitative CT for COVID-19.	The article is critically organized. Clear and detailed background. The role of CT imaging in detecting the infection was clearly stated.	Lack of a clear definition of artificial intelligence.	Medical imaging is an integral tool in the fight against COVID-19 using a collection of modalities (i.e. CXR, CT, MRI, and US)
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls	Aims to tackle the topic of AI, namely, its shortages and limitation in terms of COVID-19 response.	Provides comparisons of relevant studies	Lack of visual data analysis to provide additional comprehension and illustration of the proposed findings.	AI measures are at early stages to be applicable.
A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images	Study focuses on utilizing a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients.	Information was well organized in details.	Short study duration which may affect the results and the proposed model efficacy	The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists.
Digital technology applications for contact tracing: the new promise for COVID-19 and beyond?	Discusses the digital technology applications that are used for the rapid tracing and notification of potentially infected people.	Included a table that illustrates the worldwide implementation of digital health technology for COVID-19 contact tracing.	There was no clear definition of Artificial intelligence (AI).	Digital technology can complement or in some cases amplify the traditional approach to global health program implementation.
Automated detection of COVID-19 cases using deep neural networks with X-ray images	Aims to explore a new model for automatic COVID-19 detection using raw chest X-ray images.	Images and graphs provided extra clarification to the reader.	Small sample size of X-ray images	This system can be used from remote places in COVID-19 affected countries to overcome a shortage of radiologists.
Impact of the digital divide in the age of COVID-19	Show the impact of Digital Divide.	Information was well organized in details.	Insufficient references to support the conclusion.	The diminished accessibility to technology based on various social factors, sometimes referred to as the digital gap or digital divide, was being exposed at a critical time in a public health crisis.
Diagnosis of COVID-19 Pneumonia Using Chest Radiography: Value of Artificial Intelligence	To develop an artificial intelligence algorithm to differentiate COVID-19 pneumonia from other causes of CXR abnormalities.	The use of flowcharts, diagrams, tables, X-ray images clearly demonstrated the data.	The collected data may not reflect the true prevalence of the disease.	An artificial intelligence algorithm differentiated between COVID-19 pneumonia and non-COVID-19 pneumonia in chest x-ray radiographs with high sensitivity and specificity.
On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities	This review discusses radiologists’ opinions and suggestion trends and challenges that need to be addressed to effectively streamline interpretability methods in clinical practice.	Provides clear insights on each discussed modal including illustrative examples, charts, definitions, and operational work scenarios.	The study fails to discuss the implementation of presented methods in COVID-19.	Interpretability methods can improve understanding, trust, and verification of radiology artificial intelligence systems.
COVID-19 Detection using Artificial Intelligence	To develop a novel AI screening platform for usage and dissemination of COVID-19 x-rays.	The findings are confirmed with adjunct charts and tables and defines common AI concepts including deep learning, CNN, pooling, and convolutional layers.	The sample size is relatively small to promote generalization and validation of the proposed framework	The proposed model achieved sensitivity of 100 %, specificity of 100 %, accuracy of 100 %, PPV of 100%, and NPV of 100% in the dataset.
Advanced Digital Health Technologies for COVID-19 and Future Emergencies.	This article describes how digital health technologies are being or could be used for COVID-19 mitigation.	The article is well structured and critically organized	Lack of detailed discussion of the proposed technologies.	Digital technologies are capable of mitigating COVID-19 using a diverse range of applications to address the virus novelty.
Current Landscape of Imaging and the Potential Role for Artificial Intelligence in the Management of COVID-19	To review the current landscape of imaging modalities and artificial intelligence approaches as applied in COVID-19 management.	Clearly states significant information of the current imaging paradigm on COVID-19 in addition to AI modals.	Lack of some additional radiographic modalities (i.e. MRI and US).	Artificial intelligence can enhance the predictive power and the utilization of these imaging approaches.
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19	To cover the scope of AI-assisted diagnostics and relevant clinical tests aimed to screen, study, detect, and diagnose in terms of COVID-19.	The review article provides a simplified outline of AI associated data inclusive of applications and future research prospects.	The paper lacks an explanation of the methodology applied in article selection and inclusion in the study findings.	This paper talks about how AI gives protected, exact and productive imaging arrangements in COVID-19 applications. Two modalities X-ray and CT are utilized to shows the adequacy of AI engaged clinical imaging for COVID-19.
End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT	Autonomous differentiation of viral pneumonia from COVID-19 computed tomography findings.	The usage of figures and tables clarify the scope of the paper	The sampling population is relatively small which may impede the generalization of the proposed modal.	The novel AI platform generated a maximum specificity of 91 % and a sensitivity of 92% during the study’s training, testing, and validation stages.
Emerging Technologies for Use in the Study, Diagnosis, and Treatment of Patients with COVID-19	The purpose of this review is to summarize emerging technologies being implemented in the study, diagnosis, and treatment of COVID-19’.	Clear explanation of emerging technologies including artificial Intelligence Well and detailed information about the treatments and type of medication and vaccination.	Lack of detailed discussion of the proposed technologies’ limitations.	The advent of COVID-19 helped motivate researchers to investigate the scope and evidence related to technology’s efficacy against COVID-19.
Interpretable artificial intelligence framework for COVID‑19 screening on chest X‑rays	Development of a feasible AI model in terms of image interpretation of COVID-19 CXRs using transfer learning techniques and the evaluation of an expert radiologist panel.	The study utilized several quantitative metrics including sensitivity, specificity, accuracy, and area under curve.	Lack of training the AI module using larger sample size and additional input from experienced radiologists.	The transfer learning model was capable of undertaking binary, ternary, and quaternary at the area under curve of 1 during the management of a 5 stage dataset.
Applications of digital technology in COVID-19 pandemic planning and response	To provide a framework for the application of digital technologies in pandemic management and response.	Well summarized table of digital technology initiatives used in pandemic preparedness and response identifying every country that used that type of initiative.	No notable limitations were mentioned.	Successful repurposing of technology allowed several countries to flatten the curve of COVID-19 in their respective localities.
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia	This study aimed to establish an early screening model to distinguish COVID-19 from healthy cases through pulmonary CT images using deep learning techniques.	Information was well organized in details.	The study is limited to only one district in in China.	The proposed model achieved an overall accuracy rate of 86.7%, and would be a promising supplementary diagnostic method for frontline clinical doctors.
Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software	Triage of COVID-19 pneumonia using the deep learning software uAI.	A well-organized study was conducted using CT and uAI.	The sampling population is relatively small which may impede the proposed modal generalization.	The uAI Intelligent Assistant Analysis System detected COVID-19 pneumonia in addition to COVID-19 CT findings (i.e. GGOs and lobular lesions)
AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT	Evaluation of AI integrated image interpretation workflow in the differentiation of COVID-19 and other pulmonary findings on chest CTs.	The study is relevant and novel for demonstrating the effect of AI augmentation on radiologist performance in distinguishing COVID-19 from pneumonia of other etiology on chest CT.	There was a significant difference in baseline characteristics between COVID-19 and non-COVID-19 pneumonia patients which could have introduced bias.	Deep learning apparatus helped radiologists to improve the diagnostic performance in terms of COVID-19 at a 90% accuracy, 91% specificity, and 88% sensitivity.
Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs	Classification of COVID-19 severity on portable CXRs using deep learning CNNs and an expert radiologist panel.	Information was well organized in details.	Lack of detailed discussion of the proposed approach’s limitations.	The deep learning CNNs accomplished a comparable staging accuracy to the three-member radiologist panel at a mean absolute error of 8.5%.
How Might AI and Chest Imaging Help Unravel COVID-19′s Mysteries?	This article describes how AI has the potential to expand the role of chest imaging beyond the debatable realm of diagnosis to risk stratification, treatment monitoring, and potential discovery of novel therapeutic targets.	Information was well organized in details.	Summary/abstract was very short and not specific.	AI technologies can help to address multiple aspects of medical imaging during COVID-19 and beyond.
Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review	To discusses the application of machine learning and AI during COVID-19 using multiple encounters reported worldwide.	Employs summarization tables enabling complete comprehension and illustration of the presented information.	Lack of information about the integration of AI in COVID-19 related medical imaging.	COVID-19 allowed machine learning to surface in the medical community. Thus, exposing healthcare professional to contactless care.
Combat COVID-19 with artificial intelligence and big data.	To discuss the role of AI and big data in the mitigation of COVID 19, namely, Asian countries.	The article includes a summarization table that ensures additional comprehension of the presented information and offers background information on COVID-19 and the history of contact tracing apps in global epidemiological responses to infectious pandemics.	minimal information on the additional healthcare applications of AI during COVID-19. No other global contact tracing apps used in the mitigation of COVID-19 were discussed.	Effective COVID-19 responses are governed by the successful use of technology and public compliance to the digital interventions.