key: cord-1048763-7ef4l10u
authors: Galetsi, Panagiota; Katsaliaki, Korina; Kumar, Sameer
title: The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19
date: 2022-04-12
journal: Soc Sci Med
DOI: 10.1016/j.socscimed.2022.114973
sha: ab9a75093ad99a4ff640fee7119cce2ef142a237
doc_id: 1048763
cord_uid: 7ef4l10u

With Covid-19 impacting communities in different ways, research has increasingly turned to big data analytics (BDA) and artificial intelligence (AI) tools to track and monitor the virus's spread and its effect on humanity and the global economy. The purpose of this study is to conduct an in-depth literature review to identify how BDA and AI were involved in the management of Covid-19 (while considering diversity, equity, and inclusion (DEI)). The rigorous search resulted in a portfolio of 607 articles, retrieved from the Web of Science database, where content analysis has been conducted. This study identifies the BDA and AI applications developed to deal with the initial Covid-19 outbreak and the containment of the pandemic, along with their benefits for the social good. Moreover, this study reveals the DEI challenges related to these applications, ways to mitigate the concerns, and how to develop viable techniques to deal with similar crises in the future. The article pool recognized the high presence of machine learning (ML) and the role of mobile technology, social media and telemedicine in the use of BDA and AI during Covid-19. This study offers a collective insight into many of the key issues and underlying complexities affecting public health and society from Covid-19, and the solutions offered from information systems and technological perspectives.

4 split in societal perceptions on public health issues such as vaccination and medical protocols (Ward et al., 2020 ).

An aspect that should not be overlooked about effective implementation of Covid-19 BDA applications based on DEI principles is lack of common understanding of such principles in different parts of the world. This impedes the adoption of a global mindset for a common DEI approach. Initiatives must be localized to avoid the appearance of not relevant or not culturally tailored diversity mandates (Goodman, 2013) . In the implementation of any localised strategy; local laws, regulations, and societal norms need to be acknowledged and the systems and processes established must suit the way things get done locally (Goodman, 2013).

The current focus of DEI is based on North American social and political context (Majmudar & Kymal, 2020) , which takes away the attention from the rest of the world where DEI might be of greater importance but with a different focus.

A search in the international literature for reviews related to BDA, AI, and Covid-19 revealed a lack of articles that analyze both the medical and social impact of BDA/AI applications for managing the pandemic. There are few informative papers that profile research in BDA discussing various aspects of modern technology used to tackle Covid-19, including medical image processing, disease tracking and prediction outcomes, computational biology, and medicines (Jia et al., 2020) . There is also a number of opinion papers on similar issues (Dwivedi et al., 2020; Kulkarni et al., 2020a; Sheng et al., 2020); reviews and profiling papers relevant to a specific BDA method, such as AI (Pham et al., 2020; Chiroma et al., 2020) or to a certain aspect of Covid-19 diagnosis, such as chest X-rays and CT scan imaging analysis (Ozsahin et al., 2020) or reviews and overviews of mHealth (Islam et al., 2020) , telemedicine (Battineni, Chintalapudi & Αmenta, 2020) and social media analysis (Alhumoud, 2020) . However, none of these investigate the medical and social aspects of the use of BDA and AI for Covid-19. Therefore, this study explores the usefulness of BDA for tackling under a social and medical approach. It especially examines AI methods and new applications of big data analysis, their positive or negative effects on society and the pandemic and what more can be done. Through a DEI lens, this research examines whether the developed BDA and AI for Covid-19 applications are available to all individuals and communities. Specifically, it examines capacity requirements, for full participation of community in the provided medical services created by these J o u r n a l P r e -p r o o f advances (equity). Further, it looks at how to cater to the most disadvantaged to be able to use the provided information and technologies (inclusion) by capturing and supporting diversity based on individuals' and societies' demographic characteristics, religion, culture, and so forth.

Overall, the study addresses the following research questions. The main contribution of this research is to identify the significant applications that analyse big data in response to Covid-19 and discuss their direct and long-term benefits to healthcare and the society along with their limitations and challenges under the lens of DEI. This research also identifies to develop additional responsible BDA applications for similar cases. Identifying and recognising the positive and negative effects of rapid advances in technology can assist policymakers, scientists, and technology developers to avoid malfunctions and provide diverse and equitable healthcare to all population segments.

We followed the key principles of the PRISMA methodology to conduct the literature review. The methodology includes three stages to synthesize the themes of 6 Covid-19. It also includes terms that ensure we do not miss articles that use specific, popular BDA techniques, as identified from an initial literature screening. Our keyword list allowed us to maximize the number of articles in our dataset. The detailed search strategy is provided below. Studies published online in 2020 (including early publications of 2021) were retrieved from the WoS a year after the first appearance of Covid-19. The keywords "Big Data", "Big Data Analytic", "Artificial Intelligence", "Machine Learning" and "Mobile app" were combined with the keywords "Pandemic", "Epidemic", "Coronavirus", "Covid-19" and "SARS-Cov-2" on a one-to-one basis. Only journal research papers and review studies that were written in English and relevant to Covid-19 and BDA were included in the dataset. We included only articles and reviews to capture the full information of a study, which is usually better presented in a published journal article. The initial keyword search retrieved 985 records, but from an abstract scan, only 607 papers were finally included in our dataset after applying the inclusionexclusion criteria. Content analysis of these papers was conducted January-July 2021.

The second stage of the research process focuses on grouping and classifying the papers into selected topics by capturing the relevant texts with the use of the NVivo software after full-text review. The categories and their sub-categories, which act as the guide to the dataset content analysis, were inspired by recent literature in the health BDA field (Galetsi & Katsaliaki, 2020). After reviewing all 607 papers we established 15 sets of BDA/AI Covid-19 applications based on their contents and intentions. We assigned these papers into the 15 identified sets of applications and then allocated these sets into two topics based on their targeted entities: 1) public healthcare and 2) individuals and community. Within the pool of articles, we have identified around 30 papers that relate to DEI, Covid-19 and BDA/AI together, and we used them to drive our discussion of identifying the social challenges for each set of applications.

We applied text retrieval methods to capture specific information from the papers. The relevant section that explained its link to a sub-dimension was recognised and coded by the NVivo software.

The third stage of the methodology, output, presents the results of the classification process. First, a profile of the dataset's content is presented (e.g., publishing journals, institutions, data types used) and the highlights of citation, co- Figure 1 outlines the research methodology strategy.

This section presents an overview of our dataset demographics, including country of origin and authors' affiliation, publishing journals, subject areas, most cited and co-cited papers and authors. We provide statistics on generic paper classification, Covid-19 research in various disciplinary fields, BDA capabilities and techniques, and sources of data. We also identify various stakeholders of the BDA/AI applications.

The dataset includes publications from 61 countries. USA is first with 175 published articles (counting the number of authors affiliated with that country), followed by China (114) and India (80) . Overall, the 607 papers are published in 290 different journals, with IEEE Access and the Journal of Medical Internet Research having the highest number of publications. The four most popular journals are the first publications using data from patients in Wuhan, China's hospitals (Huang, et al., 2020; Chen, et al., 2020 where the pandemic began.

The dataset comprised a range of paper types. Most of them were experimental papers (65.0%), followed by reviews (16.0%), opinion papers (9.2%), case studies (8.2%) and surveys (3.3%). In the fields that were investigated with the aid of BDA techniques, most papers were about public health (61%), followed by human behaviour (10.5%), social science (9.2%), business (7.7%), pharmacology (5.1%), environment (2.6%), tourism (2%), and other. Many papers were classified under more than one field. The developed methods utilized the evaluation: BDA capability by 36.7%, the prediction capability (29.5%), monitoring (23.4%), reporting (12.5%) and data mining (8.3%). The BDA techniques employed in mitigating the effects of the pandemic were usually AI methods, such as ML 9 patients as they were offered diagnosis and ways to tackle their disease. Other stakeholders were IT specialists who, with their valuable contribution, designed the desired systems, physicians and health professionals who were provided with tools that helped them understand the disease, and policymakers who receive information from these applications about the current and future progress of the pandemic for taking appropriate measures.

This classification attempts to map the knowledge in the field and explains BDA and AI impact in tackling Covid-19. Therefore, in this section we offer a list of significant BDA/AI applications that were developed to deal with the pandemic.

Tables A1, A2 and A3 in the appendix present the main set of applications based on their target-group: public healthcare, individuals and the community. Each table reports for each set of applications the indicative BDA/AI technique/method, the immediate healthcare benefit and enduring societal benefits derived from such techniques and methods, and their associated challenges faced by society The last column in these tables reports the frequency of the research studies associated with each set of applications (N), and the second column provides an indicative reference for each application category (Key Ref). This indicative reference is selected either based on popularity (number of citations) or ease of understanding its current use.

According to Table A1 (appendix), nine different types of models/applications that refer to public health and medicine were identified in the literature. The majority of BDA models were developed by using AI on clinical datasets for evaluation and prediction to make medical treatments more efficient.

In particular, the first category of applications focuses on the identification of Covid-19 positive patients. Some of the 185 papers focus on detecting SARS-CoV-2 from chest CT scan images (Ardakani et al., 2020) or from chest x-ray images (Brunese et al., 2020). Novel AI models using chest images from coronavirus patients (initially retrieved from collaborating Chinese medical centers) were developed by researchers in university medical schools and biomedical engineering J o u r n a l P r e -p r o o f centers specialized in image processing and cardio-thoracic imaging. This chest imaging data trained the ML algorithms to identify whether the patient is covid positive. Such AI models/toolkits can easily be deployed worldwide to other hospitals' radiology departments, either online or integrated into their systems.

Because these models are highly sensitive and can diagnose unclear cases, they can provide a second opinion to radiologists and physicians.

This pool of papers also focuses on detecting SARS-CoV-2 from blood tests, All methods (CT, x-ray, blood tests) seem to bring results that are quite accurate, though not of the same accuracy. This is important when considering countries with different levels of medical technological resources. These Covid-19 detection methods are fast, widely available and do not impose substantial cost. Even the chest imaging AI models can be implemented in any radiology department providing the opportunity to also be implemented in deprived areas via telecommunication analysing the images remotely (Ardakani et al, 2020) . These applications can gain society's trust because they can provide accurate results creating the sense of a widely available and accessible system that is not influenced by personal or human bias (Nouri, 2021). The majority of developed countries appear to embrace technological advances by providing social status rewards to innovation and holding more patents for inventions including patents related to Covid-19 (Fey et al., 2020) . However, certain cultures may be more skeptical towards technological advances, which deprives them of participating in the testing of new diagnostic tools and therapies (Drissi et al., 2020) . In a cyclic way, this skepticism may lead to exclusion of these societies because of their absence from J o u r n a l P r e -p r o o f the development phase which may also create reservations for the use of such Covid-19 detection models.

In the second category, we identified systems that predict and monitor whether Covid-19 will spread in the population, forecasting the progress of the outbreak and the relevant policy decision scenarios such as "no action," "lockdown,"

and "new medicines" (Alanazi et al., 2020 , Allam et al., 2020 . Another study in this area proposes a "bioinspired metaheuristic" model, which simulates how Covid-19 spreads and infects healthy people from the primary infected individual (patient zero) using data such as reinfection probability, spreading rate, social distancing measures and traveling rate to simulate Covid-19 activity as accurately as possible (Martinez-Alvarez et al., 2020). Another novel application is a drone model, equipped with a thermal vision camera to detect human body temperature in order to monitor the spread of the disease in the population (Manigandan et al., 2020) . We also observed the use of ML algorithms to identify possible Covid-19 cases more quickly using phone and web surveys (Rao & Vazquez, 2020) . Sentiment data such as spatio-temporal data detecting human mobility have been used by researchers in various fields (e.g., computer scientists, statisticians, and epidemiologists) in order (Li & Guo, 2020) . Based on the predictions of these novel models, policymakers make decisions about population movement restriction measures. The variety of these applications provide outcomes based on the real situation, helping governments issue lockdown policies only in specific places, instead for the whole country, to balance the human rights of free movement against the health risks.

However, pursuing such applications comes with challenges such as personal rights violation because restriction measures such as quarantines, canceling mass gatherings, isolation may conflict with ethical and religious principles. Moreover, tracing and tracking the movement of infected people (like they do in Singapore) also violates people's privacy, so people might not comply with these measures (Nguyen et al., 2020). Furthermore, these prediction models use real-time, regional data, which assumes that data collection takes place locally and repeatedly, but not all regions or even countries have the technological and financial resources or the expertise to develop prediction models for continuous Covid-19 monitoring.

The third category of applications refers to models predicting mortality risk.

Some of the models forecast the mortality rate in specific countries .

Many studies focus on defining the right parameters for predicting mortality from (Tai et al., 2020) . However, because socioeconomically disadvantaged people from low income and education levels face barriers to obtaining, processing, and understanding basic health information and following instructions, they may not be able to participate in national level studies and health interventions (Stormacq et al., 2020) , such as the studies that require mobile apps and wearable sensors for their analysis. As a result, social minorities may be under-represented in such population observations and therefore may not obtain the appropriate treatment when needed (Kirby, 2020) . Studies have shown that widely used algorithms to identify high-risk patients were significantly biased against race groups, revealing systemic inequalities that led to poor access to care for such groups (Röösli, et al., 2021) . Moreover, heavy reliance on AI models for optimal allocation of limited resources J o u r n a l P r e -p r o o f for tackling Covid-19, such as ventilators, ICU beds, lead to delicate decisions that may provide a false sense of objectivity and equity while diverting scarce resources from regular healthcare services (Laudanski et al., 2020) .

In the fifth category, we included models that intend to create warning systems for society by using data from several sources to look for Covid-19 presymptoms. We also found ML models that combine disease estimates from digital traces, such as official health reports, Covid-19-related internet searches, and news media activity to forecast Covid-19 activity in certain areas in real-time (Liu et al., 2020) . Also, there are digital approaches such as contact tracing apps to manage population mobility and to provide the public with dynamic and credible updates on the Covid-19 pandemic (Nakamoto et al., 2020). Patient behaviour data are gathered from mobile apps, such as from GPS and from beacons or QR codes that may reveal the spatial location of the users. Based on these data, the models reveal congested population areas so that health policymakers and governments can release restriction warnings (Simmhan et al., 2020) . Similarly, in this category we also include studies using electronic wearable devices for immediate sensing of Covid-19 clinical symptoms, such as fast heart rate (Pépin et al., 2020). However, ethical concerns have been raised about the broad use of contact tracing technology and therefore app developers are prompted to build more trustworthy platforms for collaborative use of raw individual-level data which will ensure that private information is not used against certain social groups (Chatfield & Schroeder, 2020). Documentation, validation and explainability of these platforms is a first step regarding the transparent use of such intellectual property (Luengo-Oroz et al, 2020).

Transparency is necessary to understand intended predictions, target populations, hidden biases, class imbalance problems including the ability to generalize emerging technologies across hospital settings and populations (Röösli, et al., 2021) .

In the sixth category of applications, we identified models that focus on drug populations. However, the complex nature of AI solutions may also lead to biased output because of the unpredictable or unexpected occurrences in the internal data analysis process that inform about false alarms and emergent measures in society (Sipior, 2020) . Due to possible bias, these systems denote their accuracy percentage for their prediction, however, this percentage might also be miscalculated (e.g., rapid test results accuracy). It is known that actuarial risk algorithms in the US health insurance industry affect millions of patients and may exhibit significant racial and other biases at a given risk score. Specifically, in cases of false test results, the algorithms may result in decisions causing unequal access to care as health systems rely on such prediction algorithms to classify patients with complex health needs (Obermeyer et al, 2019 ).

In the eighth category, we find ML algorithms that are used as vaccinology tools to investigate the entire SARS-CoV-2 proteome, which is crucial to viral adherence and host invasion, in order to induce high protective antigenicity ( 

Continuing with the next set of applications pioneered during the Covid-19 pandemic that are targeted to individuals or communities, we come across models that report populations' mental health impacted from Covid-19. An example of this is the design of a sophisticated AI chatbot, on a smartphone application, that can diagnose and recommend immediate measures to psychologically distressed patients who have been exposed to the virus (Battineni, Chintalapudi & Amenta, 2020). Such 

This section reports the findings of concerns as stated by researchers in their studies included in the dataset and their suggestions for overcoming them. BDA and AI provided fast and efficient solutions to face the outbreak but also created numerous challenges. The previous section presented evidence that scientific efforts to find technological solutions to limit the effects of the pandemic effected society positively and negatively, especially in terms of DEI. Table A3 (appendix) summarizes six main categories of concerns, the main approaches that these studies propose for mitigating them, an indicative reference from our dataset, and the number of studies that refer to each concern.

The most recognized challenge, appearing in 154 papers, concerns the ethical issues of privacy, the use of personal data to limit the pandemic spread, and the need for security to protect data from being overused by technology. Digital technologies could be abused by benign users, malicious attackers, public authorities, and other powerful players in social media, compromising integrity and confidentiality and Researchers call for collective efforts from multiple parties, including governments, health agencies, practitioners, service providers and users, with the common objective to build security and privacy defense lines that cover both technical and social aspects. Researchers highlight the need for strong legislative protection, such as the General Data Protection Regulation (GDPR), the e-Privacy Directive, and the European Charter of Human Rights to safeguard the right to privacy and data protection (Gasser, 2020). They also advocate for even more specific protocols, such as the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT) for development of apps that monitor the spread of the disease and alert people if they have come into contact with a Covid-19 positive case. Despite these guidelines and laws, the consensus amongst the technical community is that some of these frameworks are too academic for practical development (Li & Guo, 2020) . The right direction is to develop apps with decentralized architecture, wherein the personal data is enclosed and controlled by individuals on personal devices, instead of the centralized architecture in which personal data collected through the app is controlled by government authority, which is currently the case of most such apps (Li & Guo, 2020) . Even though health data governing, and new legislative proposals increasingly focus on privacy by limiting or controlling access to health-related data, implementation of more inclusive strategies is necessary for protecting such data.

These strategies must go beyond a pure privacy focus and extend to preventing or penalizing uses that could harm individuals (McGraw & Mandl, 2021).

Another possible challenge of BDA/AI applications is the biased outputs that may result from hastily monitoring the pandemic to offer solutions-the fast collection of not so "clean" data and circumventing some validation model checks.

AI systems are built on learning from data, and if the data is skewed, it can have major consequences. Therefore, outcomes from analytics systems may be biased and perform poorly (Kiener, 2020) . The teams developing AI Covid-19 applications in organizations may not be diverse enough to build inclusive applications that reflect J o u r n a l P r e -p r o o f the diversity of the general population (Nouri, 2021). If these AI Covid-19 applications are not appropriate because of the aforementioned reasons, then it is a case of wasted scarce resources which could have been used for more pressing societal needs.

In the previous section, we argued that AI solutions are essential in reflecting the changed circumstances of life imposed by Covid-19; however, because of the complexity, the degree of confidence in AI results or datasets must always be examined (Sipior, 2020) . Since biases may exist in all BDA phases -from how the model is designed, developed and deployed to the quality, integrity and representativeness of the underlying data sources -developers must consider (or be required by national or global authority directives) addressing these biases, and physicians should recommend policies while considering the biases of parameters due to the need for fast solutions (Sipior, 2020) . (Kaushik, Patel & Dubey, 2020) . For this reason, scientists raise concerns about social inequalities due to the unavailability of digital tools and services. They also point out the absence of regulatory authorities avoiding malpractices and, therefore, the need for an appropriate body to consider solutions for patients' data ownership and acceptance of digital health. This goal is to minimize inequity and inequality, and also to raise money to build appropriate smart infrastructure worldwide as the spread of the pandemic does not follow country boundaries (Ndiaye et al., 2020).

There are also concerns about the so-called "immunity ethics" and the societal consequences of Covid-19 immunity passports and the obligation to get et al., 2020) . Therefore, policymakers should consider worldwide education strategies in BDA in terms of each country's workforce (Paudel, 2021 ).

An analysis of our dataset also reveals a future agenda of BDA/AI opportunities that offer solutions to Covid-19 or other crises.

This pandemic experience has shown societies that future living will need to be adaptable. Digital technology can provide opportunities to respond to many future societal challenges and scientists should create future smart ecosystems for collecting, analysing, and sharing real-time information and performance benchmarks to be used by health service providers and policymakers (Marston, Shore & White, 2020). New ML and soft computing models should be invented to predict outbreaks and the complex variations in their behaviour across nations by providing benchmarks (Ardabili et al., 2020) . Moreover, biobanks should be integrated into healthcare systems, which can preserve the biological material and host prospective cohorts and material related to clinical trials so that research infrastructure can offer access to materials for future medical crises (Holub et al.,

Future applications must also focus on more complex frameworks that can instantly analyse high volumes of emerging clinical trials on therapies, such as those for Covid-19. Also, these new applications, combined with emerging disciplinaries such as bioinformatics and cheminformatics, should target structure-based drug designs, network-based methods for prediction of drug-target interactions, and work with AI, ML and Phage techniques to provide alternative routes for discovering patent drugs (Omolo et al., 2020) .

Also, social media can further be exploited to offer novel insights. Since

Covid-19 revealed that people can experience symptoms for many weeks as well as post-covid symptoms that may change over-time, data about patient experiences could be used to develop rapid assessments of large numbers of social media conversations to monitor public health (Picone et al., 2020).

Our study reviewed the publications on the Covid-19 pandemic that use BDA and AI algorithms. After providing a dataset profiling of relevant papers, we focused great debate has also started about some countries' decisions to use compulsion rather than persuasion for their immunization programs and the possibility such decisions do more harm to the well-being of free people than good (Pennings & Symons, 2021) . This is also relevant to the discussion of immunity passports which will allow individuals to return to their daily activities but raise immunity ethics concerns regarding pros and cons for the society.

Overall, Covid-19, as a threat to world-wide well-being, has led to vast research into BDA for Covid-19 and the rapid "commercialization" of research. The need for urgent solutions has led researchers to shorten their models' validation processes to produce fast treatment outcomes. It is hopeful, however, that this need has brought information disclosure related to research about vaccines and drugs formulas that prevent/treat the virus. However, pharmaceutical patents have J o u r n a l P r e -p r o o f restricted access to generic supplier companies to develop the vaccine (Siegel & Guerrero, 2021) .

The articles also identify the future direction of these applications, describing experimental models and systems that explain human-machine interactions and promote approaches for better data management in unpredictable situations (Iandolo et al., 2020) ; digital technology that create smart ecosystems to respond to possible health crisis by mitigating diversity, equity and inclusion challenges (Marston, Shore & White, 2020); and prediction models of outbreaks that incorporate variations in the behaviour across nations and biobanks (Holub et al., 2020).

It is a great opportunity to learn from the pandemic and its accelerated technology advancements attained in a short time. We can learn how to use related BDA/AI technologies to deal with similar humanitarian disasters in the future. There is also a great need to address the unintended consequences of Covid-19 with BDA/AI technologies.

For example, the models for optimizing Covid-19 patient management in healthcare centers (Table A1 -group 4) that focus on appropriate hospital resources allocation can also be used to deal with the re-allocation of healthcare delivery resources (e.g., physicians, beds, surgical theatres). These models can address the unintended consequences of Covid-19 such as the prolonged postponement of elective surgeries and treatments which have surmounted during the pandemic making people's general health deteriorate.

In addition, this pandemic has created many other economic and societal challenges due to social isolation and increased unemployment. These challenges include the increase in mental health cases among children and adult population, addiction to internet usage, agoraphobia, and poverty. Therefore, improving and increasing use of models for mitigating populations' mental health impact from Covid-19 (Table A2 -group 1) is very important to be able to identify psychological distress and addictions and provide these people healthcare resources to fight the problems. Such applications can be improved and used through a mobile app which will monitor patients by scheduled questions related to their health and a chatbot that can provide appropriate responses, through text classification and trained ML algorithms. In combination with behavioral data received by the smartphone (such as phone activity, step counter, sleep, and heart-rate monitor) and video-call J o u r n a l P r e -p r o o f capabilities, a doctor can monitor and manage the patients and intervene with telemedicine when necessary.

The same applies to models for providing personalized telehealth (Table A2- group 2) as this may become the new reality of health services not only for teleconsultation but also for more healthcare tasks, such as measuring vital signs using a mobile app. The increasing use of such models can enhance patient management with patient support systems for automated messages, such as appointment reminders/bookings, clinical results release, and drug prescriptions through an authentication process. Such telehealth options will provide more efficient service and give access to more people, especially all vulnerable people or those leaving in rural areas.

Additionally, with small adaptations in the collected data and in the spread patterns, models for predicting the spread of Covid-19 in the community (Table A1- group 2) can be used for other diseases in the future and for different regions. Models measuring the spread and tension of misinformation for Covid-19 (Table A2-group 2) can be used for other situations of breaking news to flag misinformation.

Models such as those for the immediate identification of Covid-19 positive cases from CT chest images (Table A1- These new health applications could shape the future of healthcare and improve society's well-being. However, before such apps are released in the market, many issues must be resolved related to data governance and equity for their availability to all segments of world population.

This article illustrates how the pandemic accelerated the adoption of digital Commercializing science and AI algorithms into usable applications in dayto-day healthcare could bring certain benefits to society, medicine and business, and ethical practices could reduce the negative impact on society. The first step to minimize the risk and provide safe healthcare to humanity is recognizing and identifying the possible effects of using innovative technology when technology becomes the basic road of survival in case of a sudden attack, such as the recent pandemic.

BDA applications created opportunities to identify who is getting sick and dying and who is in danger of being affected, helping governments to take social distancing measures and public health agencies to direct money and resources to the 

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Promoting inclusion, diversity, access, and equity through enhanced institutional culture and climate

CPAS:the UK's national machine learning-based hospital capacity planning system for Covid-19

Considerations of diversity, equity, and inclusion in mental health apps:A scoping review of evaluation frameworks

Identification of Covid-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine

CovidSens:a vision on reliable social sensing for Covid-19

Bias at warp speed:how AI may contribute to the disparities gap in the time of Covid-19

Increasing global awareness of timely Covid-19 healthcare guidelines through FPV training tutorials: Portable public health crises teaching method

Exploitation of artificial intelligence for predicting the change in air quality and rain fall accumulation during Covid-19

Performing an informatics consult: methods and challenges

Pandemic in the New Era of Big Data Analytics: Methodological Innovations and Future Research Directions

A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of Covid-19 in India

The impact of quarantines, lockdowns, and 'reopenings' on the commercialization of science: micro and macro issues

GoCoronaGo: privacy respecting contact tracing for Covid-19 management

Considerations for development and use of AI in response to Covid-19

Effects of health literacy interventions on health-related outcomes in socioeconomically disadvantaged adults living in the community: a systematic review

A survey on deep transfer learning to edge computing for mitigating the Covid-19 pandemic

The disproportionate impact of Covid-19 on racial and ethnic minorities in the United States

The essential role of technology in the public health battle against Covid-19

The French public's attitudes to a future Covid-19 vaccine: The politicization of a public health issue

Novel coronavirus (2019-ncov)

Machine learning for clinical trials in the era of Covid-19

How should artificial intelligence screen for skin cancer and deliver diagnostic predictions to patients?

Ensuring that biomedical AI benefits diverse populations