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European Journal of Social Impact and Circular Economy - ISSN: 2704-9906  
DOI: 10.13135/2704-9906/5067 Published by University of Turin http://www.ojs.unito.it/index.php/ejsice/index 
EJSICE content is licensed under a Creative Commons Attribution 4.0 International License   

84 

Artificial Intelligence to fight COVID-19 outbreak 
impact: an overview 

Davide Calandra1 and Matteo Favareto1 

1 Department of Management, University of Turin, Turin, Italy 
 
Corresponding author: Matteo Favareto – matteo.favareto@unito.it 
 
Received: 27/08/2020  
Revised: 18/11/2020 
Accepted for publication: 18/11/2020 
Published: 18/12/2020 

Abstract

Artificial Intelligence (AI) is showing its strength worldwide in the healthcare sector. Today, in the 

aftermath of the COVID-19 pandemic, the help of technology appears to be relevant to keep the increase 

in new infections stable and help medical staff in treatment. Therefore, this paper aims to investigate how 

AI can be employed against COVID-19 outbreak. Using a multiple case study approach, researchers find 

out the following insights. First, AI could be used for drugs discovery and knowledge sharing, tracking 

and prediction, clinical decision making and diagnosis, social distancing and medical chatbots. Second, 

this paper provides an in-depth analysis of international best practice for tracking contacts and social 

distance applications. Third, AI technologies could have a transversal impact, also focusing on prevention 

strategies as a new corporate social responsibility vein. In the end, this paper has theoretical and 

managerial implications, too. On the theoretical side, we contribute to the extensive discussion about AI 

and healthcare considering COVID-19 outbreak. On the practical side, we provide medical personnel and 

policymakers with a tool to understand artificial intelligence and focus investment choices in the practical 

applications analysed. 

 

Keywords: Artificial intelligence; AI applications; COVID-19; Coronavirus; Health impact; Prevention 
strategies 

 



 

 

 

1. Introduction 

The novel coronavirus disease (COVID-19) has created tremendous chaos around the world, affecting 

people’s lives and causing many deaths. The first cases were detected in Wuhan, China, in December 

2019, and now it has been spread to almost every country (Nguyen, 2020). With this growing crisis, 

companies and researchers over the world are looking for the ways to address the challenges of this virus 

considering companies and health issue, to mitigate the spread and develop a cure for this disease 

(Secinaro et al., 2020). In this baffling battle, science, and Artificial Intelligence (AI) technologies are 

playing a vital role (Kumar et al., 2020).  

AI commonly refers to the computational technologies that mimic or simulate processes supported with 

human intelligence, for instance, reasoning, deep learning, adaptation, interaction, and sensory 

understanding (Tran et al., 2019). These are technologies that can perform a task that usually requires 

human perception and judgement (Hamid, 2016). These techniques have an interdisciplinary approach 

and can be applied to different fields, such as medicine and health. AI has been used in the field of 

medicine since as early as the 1950s when physicians made the first attempts to improve their diagnoses 

using computer-aided programs (Frankish & Ramsey, 2014).  

The interest and advances in medical AI applications have surged in recent years, thanks to the 

substantially enhanced computing power of modern computers and the vast amount of digital data now 

available for collection and utilisation (Tran et al., 2019). AI is gradually changing medical practice. There 

are several applications of AI in medicine, and they can be used in a variety of medical fields such as: 

clinical, diagnostical, rehabilitative, surgical and predictive (Jiang et al., 2017; Hamid, 2016). As 

suggested by Shi et al. (2020), AI could play an essential role in COVID-19 increasing efficiency during 

working activities and for disease diagnosis, tracking contacts and prognosis. Despite the importance of 

this field, no studies reviews and discusses the role of AI in tracking services. Therefore, this paper aims 

to investigate how AI can be employed against COVID-19 outbreak, answering to the following questions: 

What are companies and labs, around the world, doing to fight COVID-19 outbreak? What could be the 

central role of AI against COVID-19?  

To answer these challenging questions, researchers use a multiple case study theoretical and practical 

approach to developing the topic. We present the main fields where AI is currently being used and some 

organisations that are involved in those areas of research.  



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Finally, the paper is organised as follows. Section 2 will assess an in-depth literature review on the role 

of AI against COVID-19 outbreak. Section 3 elaborates on the methodology. Section 4 presents the main 

results obtained. Finally, section 5 will discuss and conclude the paper with future implication for research. 
 

2. Literature review 

2.1 COVID-19 and the need for AI technologies to support the research 
 
Naudé (2020); Vaishya et al. (2020); Yassine & Shan (2020) proposed studies in which they analysed 

how AI technologies can be used in the fight against COVID-19 outbreak. As stated by Yassine & Shan 

(2020), artificial intelligence is one of the means or avenues to understand the virus and develop 

preventative and control measures. It includes but is not limited to the usage of mathematical modelling 

to understand virus transmission, structural biology to determine virus structure and develop vaccines, 

computational biology to understand virus evolution, as well as docking studies to screen for drugs and 

inhibitors. AI has the potential to help in all the stages of healthcare, from syndromic surveillance through 

to rapid diagnosis tests, and faster drug development (Naudé, 2020; Vaishya et al., 2020; Yassine & Shan 

2020). 

According to Kumar et al. (2020), AI technologies are fundamental tools for tracking and monitoring 

COVID-19 spread around the world. These technologies are helping in diagnosing the virus and in 

processing the healthcare claims. Tracking the spread of COVID-19 can be a piece of essential information 

for public health authorities to design, plan, and deal with the pandemic (Kumar et al., 2020). As stated 

by Lalmuanawma et al. (2020), contact tracing is a crucial aspect to prevent a wider spread of COVID-

19. The process of contact tracing is to identify and manage people who are recently exposed to an infected 

COVID-19 patient to avoid further spread. In this regard, various infected countries come up with a digital 

contact tracing process with the mobile application, utilising different technologies. All these digital apps 

are designed to collect individual personal data, which will be analysed by AI tools to trace a person who 

is vulnerable to the novel virus due to their recent contacted chain (Lalmuanawma et al., 2020). 

Pham et al. (2020); Bragazzi et al. (2020) dealt with how big data and artificial intelligence can help 

better manage the COVID-19 pandemic. Thanks to the latest advancements in the field of computational 

techniques and information and communication technologies (ICTs), artificial intelligence and big data 



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can help handle the vast, unprecedented amount of data derived from public health surveillance, real-time 

epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating 

from governmental institutions and organisms, and health resources utilisation information (Bragazzi et 

al., 2020). According to Pham et al. (2020) in the context of COVID-19, big data refers to the patient care 

data such as physician notes, X-Ray reports, case history, list of doctors and nurses, and information of 

outbreak areas. Same authors stated that big data potentially provide several promising solutions to help 

combat COVID-19 epidemic. By combining with AI analytics, big data help to understand the COVID-

19 in terms of outbreak tracking, virus structure, disease treatment, and vaccine manufacturing. 

Divya et al. (2018); Ahuja et al. (2020) analysed another relevant topic linked with AI and COVID-19: 

AI-powered chatbots and virtual health assistants. While AI may assist in the discovery of novel drugs 

and vaccines, it can also help ease the stress placed on medical hotlines. Call centres nationwide that are 

dealing with the COVID-19 pandemic are notoriously understaffed. Artificial intelligence has the potential 

to revolutionise public communications and deliver alternative methods to dissipate public information. 

AI-powered chatbots have been used with success in clinical scenarios and can advise many more people 

than a staffed call centre. In conclusion, AI-powered chatbots and virtual health assistants are fostering 

social distancing and streamlining the entire clinical process (Divya et al. 2018; Ahuja et al., 2020). 

According to Mahomed (2020); Sun & Zhai (2020); Nguyen et al. (2020), AI can be used to control 

social distancing. As stated by Sun & Zhai (2020); Mahomed (2020), social distancing avoids direct 

contacts among people and reduces the potential cross-transmission of virus-carrying droplets from human 

respiration. By Nguyen et al. (2020) AI technologies play a crucial role in social distancing control. For 

instance, they allow public place monitoring and can detect, recognise, and identify whether people 

comply with social distancing requirements or not. 

Zhavoronkov et al. (2020); Mohanty et al. (2020) dealt with the topic of AI applications to find out new 

drugs and treatments. AI can be used to initiate drug discovery and fast-track drug development. These 

technologies have the potential to improve the drug discovery, planning, treatment, and reported outcomes 

of the COVID-19 patient, being evidence-based medical tools. According to Mohanty et al. (2020), AI 

can help, as well, in drug repurposing process. Drug “repurposing” refers to the use of existing approved 

drugs for the treatment of a never-considered therapeutic indication, in this case, COVID-19. The AI-



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based drug repurposing is a cheaper, faster, and practical approach and can minimise the failures in clinical 

trials (Mohanty et al., 2020; Xue et al., 2018). 

Table 1 below shows the list of related work includes in the literature review section. 
 
Table 1. List of related work 
References Field 
• Naudé (2020); Vaishya et al. 

(2020); Yassine & Shan (2020) 
• Kumar et al. (2020); 

Lalmuanawma et al. (2020) 

AI applications in the fight against 
COVID-19 
AI technologies for monitoring and 
tracking COVID-19 spread 

• Pham et al. (2020); Bragazzi et al. 
(2020) 

AI and Big data for COVID-19 
fighting  

• Divya et al. (2018); Ahuja et al. 
(2020) 

 
• Mahomed (2020); Sun & Zhai 

(2020); Nguyen et al. (2020) 
• Zhavoronkov et al. (2020); 

Mohanty et al. (2020) 

AI-powered chatbots and virtual health 
assistants to manage medical call 
centers and ease public tension 
AI for social distancing control 
 
AI for drugs discovery or repurposing 
against COVID-19 

Source: Authors’ elaboration 
 

3. Methodology 

To implement the analysis, we employed a case study approach to investigate AI applications against 

COVID-19 (McCutcheon & Meredith, 1993). For each topic under discussion will be described theoretical 

items and then practical examples of companies that are currently involved in coronavirus fight.  

Because of the virus and the crucial role of AI, nowadays scientific community and companies are 

focusing more and more on the topic. There are so many firms and labs that are studying the virus evolution 

to find out a remedy. For this reason, it would have been difficult to describe all of them. For the best of 

our knowledge, we chose only some companies, and we described strategies and contribution to fight back 

against COVID-19 spread. 

As stated before, we decided to employ a case study approach. According to Yin (2014), case studies 

can be defined as qualitative research methods that support researchers when "a how or why the question 

is being asked about a contemporary set of events over which the investigator has little or no control". 

Besides, qualitative methodologies can usually be better understood by practitioners in leading to 

managerial practices (Dal Mas et al., 2019), helping to bridge the gap between academia and practice 



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89 

(Massaro et al., 2018). Additionally, according to Crowe et al. (2011), the collective case study involves 

studying multiple cases simultaneously or sequentially in an attempt to generate a still broader 

appreciation of a particular issue. 

As suggested by Granello & Wheaton (2004), to collect data, we used different types of online sources:  

(1) corporate document and materials; 

(2) scientific papers published in peer-review journals; 

(3) YouTube interview of the founders;  

(4) newspaper articles. 

To answer the research questions of this paper, the authors use some study's limitation. Furthermore, all 

the sources extracted should:  

• deal with AI in healthcare; 

• link with COVID-19 pandemic topic; 

• describe theoretical and practical outputs in terms of COVID-19 response. 

Besides, we searched for papers that use a case study methodology to develop the analysis. 

Using the criteria mentioned above, we used Scopus and Google Scholar as databases. Thus, the first 

one is a broad scope database essential for peer-reviewed and conference proceedings analysis (Mongeon 

& Paul-Hus, 2016). Finally, Google Scholar is an international open-access database that collects 

information on academic sources in different fields (Falagas et al., 2007).   

The analysis was implemented using as keywords "Artificial Intelligence" and "COVID-19" or 

"Coronavirus" and following the objective of the article. Among the search criteria, we focused on English 

items that were inherent to the searched object. 

Overall we got 155 documents divided as follows.  

To describe companies’ projects, we consulted 20 corporate documents published on companies’ 

official websites. We analysed 55 scientific papers published in peer-review journals, and we watched 15 

YouTube interviews of the companies’ founders to understand, in a better way, the documents we got and 

obtaining updates on the topic. Besides, we referred, also, to 65 newspaper articles published by 

international journals, in different countries, and dealing with the specific topic we analysed. 

Figure 1 below shows the methodology used to carry out the analysis. 



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Figure 1. Characteristics of the analysis 

 

Source: Authors’ elaboration 

4. Results and Discussion 

AI tools and technologies can be employed to support efforts of policy-makers, the medical community, 

and society at large to manage every stage of the crisis and its aftermath: detection, prevention, response, 

recovery and to accelerate research (OECD; Naudé, 2020).  

There are different areas where AI technologies can contribute to the fight against COVID-19 (Naudé, 

2020; Vaishya et al., 2020; Yassine & Shan, 2020): drugs discovery and knowledge sharing (1), tracking 

and prediction (2), clinical decision-making and diagnosis (3), social distancing control and medical 

chatbots (4). 

Figure 2 shows dominant variables that have been analysed for AI against COVID-19 outbreak. 
 



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Figure 2. Dominant variables for AI against COVID-19 outbreak 
 

 
 
Source: Authors’ elaboration 
 

4.1 AI for drugs discovery and knowledge sharing: the DeepMind and Insilico Medicine case studies 

The first application of AI to fight the coronavirus is certainly assistance to researchers to develop drugs 

and treatments to contain the pandemic.  

Even long before the COVID-19 outbreak, AI was used for its potential to contribute to new drugs 

discovery (Naudé, 2020). AI technology is used in speeding up drug testing in real-time, where standard 

testing takes plenty of time and hence helps to accelerate this process significantly, which may not be 

possible by a human (Vaishya et al., 2020). 

In the case of COVID-19, several research labs and data centres have already indicated that they are 

recruiting AI to search for treatments for a vaccine against COVID-19 (Naudé, 2020).  

According to Ledford (2020) “It is likely to be one of the biggest drug-making challenges the world has 

ever faced”. Several companies are currently involved in this big challenge, and AI technology certainly 

can help to understand the virus and to accelerate medical research on drugs and treatments.  



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This case could consider Google’s DeepMind with the AI-based model AlphaFold. AlphaFold system 

can release structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus 

that causes COVID-19. DeepMind has used AI to predict the structure of the proteins of the virus 

information that could be useful in developing new drugs (Naudé, 2020).  

US company Insilico Medicine represents another example. It is a biotechnology company that was able 

to use its integrated AI-based drug discovery pipeline to generate new drug compounds against COVID-

19. The results revealed a novel method of developing new treatments against COVID-19 while also 

demonstrating cost-effectiveness and time efficiency (Zhavoronkov, 2020; Mahomed, 2020). Through the 

use of AI technology, the company was able to categorise thousands of molecules for potential 

medications in only four days, and these data were then made available for free to researchers (Mahomed, 

2020). 

Also, AI techniques can support physicians to analyse the thousands of research papers published 

around the world on the pandemic. Many promising initiatives, however, have been started to gather and 

share data and to train new AI models. These include the World Health Organization’s (WHO) Global 

Research on Coronavirus Disease Database, which also provides links to other similar initiatives. One of 

these is the open-access data of the GISAID Initiative. Amongst other initiatives, perhaps the most 

ambitious is the joint initiative between Semantic Scholar, the Allen Institute for Artificial Intelligence, 

Microsoft, Facebook, and others, to make openly available the COVID- 19 Open Research Dataset 

(CORD-19) which contains around 44.000 scholarly articles for data mining (Naudé, 2020). 
 

4.2 AI as an observer and predictor of the evolution of the pandemic: the BlueDot case study 

With the help of real-time data analysis, AI can provide updated information which is helpful in the 

prevention of this disease. It can be used to predict the probable sites of infection, the influx of the virus, 

need for beds and healthcare professionals during this crisis (Vaishya et al., 2020).  

In this sector, the contribution of the Canadian company BlueDot has already become remarkable. It is 

a global AI database company which uses outbreak risk software, combined their health and medical 

expertise with advanced data analytics to build solutions that track, contextualise and anticipate infectious 

disease risks (Mahomed, 2020). BlueDot predicted the outbreak of the infection at the end of 2019, issuing 



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a warning to its clients on 31st of December 2019, before the World Health Organization did so on 9th of 

January 2020 (Naudé, 2020; Kreuzhuber, 2020).  

BlueDot uses an AI-driven algorithm that scours foreign-language news reports, animal and plant 

disease networks, and official proclamations to give its clients warning to avoid danger zones. BlueDot's 

reports are then sent to public health officials in a dozen countries, airlines, and frontline hospitals where 

infected patients might end up.  

BlueDot's "Global Early Warning System" combines more than a hundred datasets with proprietary 

machine learning and Natural Processing Language algorithms in 16 languages, providing an automated 

surveillance platform that can analyse risks of the virus spread.  

Also, according to the information reported on the Council of Europe website, several COVID-19 apps 

allow tracking and prediction of the virus spread around the world (table 2). For each country is reported: 

name of the application (1), main functionality (2) and the origin (3). Data are updated on the 10th of June 

2020. 
 
Table 2. Overview of COVID-19 apps 

Country Name of the application Main functionality Origin 
Argentina Covid-19 Ministerio de Salud Self-diagnostic Governmental 

Australia Coronavirus Australia Quarantine enforcement Governmental 
Australia CORONAlert Alerting Governmental 
Australia COVIDSafe Contact tracing Governmental 
Austria StoppCorona Contact tracing Governmental 
Bahrain BeAware Bahrain Quarantine enforcement Governmental 
Brazil Coronavírus - SUS Information Governmental 
Brazil The Spread Project Contact tracing Private 
Bulgaria ViruSafe Contact tracing Governmental 
Canada Canada Covid-19 Self-diagnostic Governmental 
Canada Covid Shield Contact tracing Private 
Canada Covi Contact tracing Private 
Chile CoronApp Self-diagnostic Governmental 
China Alipay Health Code Contact tracing Private 
Colombia CoronApp - Colombia Medical reporting Governmental 
Czech Republic eRouška Contact tracing Governmental 
Denmark Smittestopp Contact tracing Governmental 



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Finland Ketju Contact tracing Private 
France StopCovid Contact tracing Governmental 
France uTakeCare Contact tracing Multistakeholder 
France Alertanoo Contact tracing Private 
Georgia Stop Covid Contact tracing Governmental 
Germany Coronika Contact tracing Private 
Germany Our Health In Our Hands (OHIOH) Contact tracing Multistakeholder 
Germany Ito Contact tracing Multistakeholder 
Ghana GH COVID-19 Tracker App NA Governmental 
Greece DOCANDU Covid Checker Self-diagnostic Multistakeholder 

Hong Kong Stay Home Safe Quarantine enforcement Governmental 
Hungary VirusRadar Contact tracing Governmental 
Iceland Rakning C-19 Contact tracing Governmental 
India Test Yourself Goa Self-diagnostic Governmental 
India Corona Watch Contact tracing Governmental 

India Quarantine Watch Quarantine enforcement Governmental 
India Mahakavach Contact tracing Governmental 
India Test Yourself Puducherry Self-diagnostic Governmental 
India COVA Punjab Contact tracing Governmental 
India Aarogya Setu Contact tracing Governmental 
India COVID-19 Feedback Medical reporting Governmental 
India COVID-19 Quarantine Monitor Contact tracing Governmental 
India GoK Direct - Kerala Not Covid-19 specific app Governmental 
India Trackcovid-19.org Self-diagnostic Private 
Indonesia PeduliLindungi Contact tracing Governmental 
Iran NA NA Governmental 
Israel Hamagen Contact tracing Governmental 
Italy allertaLOM Medical reporting Governmental 
Italy diAry “Digital Arianna” Contact tracing Private 
Italy Immuni Contact tracing Private 
Italy Rintraccia dei contatti Contact Tracing Private 
Italy SM-COVID-19 Contact Tracing Private 
Italy CovidApp - Covid Community Alert Contact Tracing Private 
Jordan AMAN Contact Tracing Governmental 
Kuwait Shlonik Self-diagnostic Governmental 
Latvia Apturi Covid Contact Tracing Governmental 



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Malaysia Gerak Malaysia Contact Tracing Governmental 
Malaysia MySejahtera Information Governmental 
Malaysia MyTrace Contact tracing Governmental 
Morocco Wiqaytna Contact tracing Governmental 
Mexico Plan Jalisco Covid-19 Contact tracing Governmental 
Mexico COVID-19MX Self-diagnostic Governmental 
Netherlands PrivateTracer Contact Tracing Private 
North Macedonia StopKorona! Contact tracing Governmental 
Norway Smittestopp Contact tracing Governmental 

Poland Kwarantanna domowa Quarantine enforcement Governmental 
Poland ProteGO Contact tracing Multistakeholder 
Qatar COVI Information Private 
 
Republic of Angola Covid-19 AO Quarantine enforcement Private 
Russia Social Monitoring Contact tracing Private 

Saudi Arabia Tawakkalna (Covid-19 KSA) Quarantine enforcement Governmental 
Singapore TraceTogether Contact tracing Governmental 
Singapore SafeEntry Contact tracing Governmental 
South Africa Covi-ID Contact tracing Governmental 
South Korea Self-Isolator Safety Protection Quarantine enforcement Governmental 
South Korea Mobile self-diagnosis Self-diagnostic Governmental 

South Korea Self Quarantine App Quarantine enforcement Governmental 
Spain STOP COVID19 CAT Information Governmental 
Spain COVID-19.eus Contact tracing Governmental 
Spain CoronaMadrid Medical reporting Governmental 
Sri Lanka Self Shield Quarantine enforcement Governmental 
Switzerland Alertswiss Not Covid-19 specific app Governmental 
Switzerland SwissCovid Contact tracing Multistakeholder 
Thailand MorChana Contact tracing Governmental 
Turkey Korona Önlem Self-diagnostic Governmental 
Ukraine Action Not Covid-19 specific app Governmental 
 
United Arab Emirates Tawakkalna (Covid-19 KSA) Quarantine enforcement Governmental 
United Kingdom COVID Symptom Study Medical reporting Private 
United Kingdom NHS App Contact tracing Governmental 
United States Coalition App Contact tracing Private 
United States COVID-19 Apple App Information Multistakeholder 



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United States CovidSafe Contact tracing Multistakeholder 
United States How We Feel Self-diagnostic Private 
United States Private Kit: Safe Paths Contact tracing Private 
United States Covid Watch Contact tracing Private 
United States NOVID Contact tracing Private 
United States coEpi Medical reporting Private 
Uruguay Coronavirus UY Self-diagnostic Governmental 
Vietnam COVID-19 Self-diagnostic Governmental 

Source: Authors’ elaboration on Council of Europe data 
 

4.3 AI to support physicians in clinical decision-making and diagnosis: the Infervision and Alibaba case 
studies 

Due to a sudden and massive increase in the numbers of patients during COVID-19 pandemic, 

healthcare professionals have a very high workload. Here, AI is used to reduce the workload of healthcare 

workers. It helps in early diagnosis and providing treatment at an early stage using digital approaches and 

decision science (Vaishya et al., 2020).  

AI applications can support doctors and medical researchers in the clinical decision-making process. 

According to Jiang et al. (2017), AI can assist physicians in making better clinical decisions or even 

replacing human judgement in specific functional areas of healthcare.  

For instance, Infervision, a Chinese high-tech enterprise in artificial medical intelligence, created an AI 

software that flags possible lung problems on Computed Tomography (CT) scans, using hundreds of 

thousands of lung images collected from major Chinese hospitals. Originally used to diagnose lung cancer, 

the software is also capable of detecting pneumonia associated with respiratory diseases such as 

coronavirus. This AI-based technology allows a better understanding of coronavirus cases and makes the 

process quicker. It represents good support for physicians in their clinical decision-making process. 

Another example is the Alibaba DAMO Academy that is dedicated to exploring the unknown through 

scientific and technological research and innovation. The technology, developed by Alibaba DAMO 

Academy and Alibaba Cloud, can analyse CT images within 20 seconds for diagnosing suspected novel 

coronavirus cases with an accuracy rate of 96 per cent. Doctors usually spend around 5 to 10 minutes 

diagnosing CT images of a patient. The AI diagnosis system can ease the already strained hospital 

resources (Pham et al., 2020). 



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Finally, robots can be used to support medical professionals in their daily routine; for example, in China, 

robots that are typically used in the catering industry are used to clean, sterilise, and deliver food and 

medicines to reduce human contact. The use of AI technologies should not attempt to replace healthcare 

professionals entirely but rather assist in improving and fast-track diagnosis while alleviating bottlenecks 

in the healthcare system (Mahomed, 2020). 
 

4.4 AI to control social distancing and medical chatbots: the Microsoft Healthcare Bot case study 

One of the biggest challenges of implementing a defence mechanism against a pandemic is to ensure 

public participation and acceptance of any mechanism proposed by the authority (Young, 2013). Physical 

distancing from one another is a critical part of ensuring that the virus does not spread.  

AI has been argued to be necessary to manage the pandemic by using thermal imaging to scan public 

spaces for people potentially infected, and by enforcing social distancing and lockdown measures (Rivas, 

2020). For example, China's sophisticated surveillance technology uses facial recognition and body 

temperature to identify whether an individual has a fever and therefore, could be a carrier of the virus 

(Mahomed, 2020). 

An AI-based computer vision camera system has been used in the UK to monitor adherence by 

individuals to social distancing measures. More controversially, Israel's cyber monitoring system enables 

its security services to identify and quarantine people who may be infected (Naudé, 2020). 

Also, several companies are investing in the development of medical chatbots based on AI. These 

technologies can screen people and advise whether they should be evaluated for the infection. They enable 

"at-home risk assessments" in a few minutes. AI-powered virtual health assistants and chatbots, enable 

self-service, drive better outcomes, and reduce costs (Divya et al., 2018). Moreover, they can ease the 

stress placed on medical hotlines (Ahuja et al., 2020). Chatbots are fostering social distancing and 

streamlining the entire clinical process.  

The Microsoft Healthcare Bot is an example of this technology. The service combines built-in medical 

intelligence with natural language capabilities, extensibility tools and compliance constructs. That service 

allows healthcare organisations such as Providers, Payers, Pharma, Health Maintenance Organisations 

(HMOs), Telehealth to give people access to trusted and relevant healthcare services and information. 



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Virtual health assistants and chatbot technology will never replace medical personnel. Nevertheless, 

technology can help make better use of medical personnel's time and relieve some of the burdens from the 

healthcare system (Bitran, 2019). 
 

4.5 AI-based technologies: a transversal impact 

As we described, AI technologies have a crucial role in the fight against COVID-19 and can be used in 

different ways and applied for a wide range of purposes. AI is not only helpful in the treatment of COVID-

19 infected patients but also for their proper health monitoring (Mahomed, 2020). It is also useful to 

facilitate the research on this virus using the available data. AI can help in developing proper treatment 

regimens, prevention strategies, drug and vaccine development (Vaishya et al., 2020). 

In this sense, AI-based technologies are essentials because they can generate a positive transversal 

impact on society and healthcare organisations (Naudé, 2020). These technologies, also, can contribute to 

the optimisation of logistics processes in hospitals and allow a better allocation of resources for firms 

(Jiang et al., 2017). 

In the end, considering prevention strategies, AI-based technologies can support companies’ enhancing 

their social responsibility (Pan, 2020). In this sense, as discussed by Zhao (2018), COVID-19 could allow 

businesses to consider alternative corporate social responsibility challenges.  

  

5. Conclusion 

Digital technology, including information technology and AI, are therefore proving to be essential tools 

to help build a coordinated response to this pandemic. In our research, we focused on central areas of 

applications where AI technologies are currently impacting against COVID-19, and we analysed some 

practical cases. Table 3 shows the elements under discussion and case studies that have been analysed in 

our paper.  

From our analysis came up with some crucial insights.  

First, we described how AI technologies play a crucial role in terms of contact tracing and monitoring 

of the pandemic spread. For this reason, governments around the world are investing more and more to 



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99 

develop COVID-19 apps. These AI-based technologies can allow storage of data that could be useful for 

tracking “hot zone” and create global alert of high-risk pandemic areas.  

Secondly, we observed that high-tech companies around the world are embracing a more healthcare-

oriented approach. Big players are even more oriented to create a positive impact on society and are 

developing new technologies that can help to fight the virus.  

This paper showed that the use of modern technology with AI improved the screening, prediction, 

contact tracing, forecasting, and drug/vaccine development with extreme reliability (Lalmuanawma et al., 

2020). Considering these platforms assists AI experts to analyse massive datasets and help physicians train 

machines, set algorithms, or optimise the interpreted data for dealing with the virus with more speed and 

accuracy (Jamshidi et al., 2020). The analysis highlights, as well, that AI techniques are a fundamental 

tool for clinical decision-making. They can be used in different ways and applied for a wide range of 

purposes. Applications based on AI simplify work for physicians and medical staff providing them 

meaningful insights.  

This paper has theoretical and managerial implications; in fact, it could be useful either for researchers 

or professionals (i.e., policymakers, physicians, managers, healthcare, and administrative staff). We 

wanted to underline that artificial intelligence applications are fundamental to contain the spread of the 

virus and to find out a vaccine as soon as possible. Analysing some AI applications, we aimed, also, to 

help healthcare organisations to allocate their financial resources better and investing more in these 

technologies. Finally, we chose to describe AI applications against COVID-19 to allow a better 

understanding of these techniques and help other professionals to gather what currently exists. 

Like any research, this paper has some limitations.  

First, the writing period did not allow the researchers to deepen their knowledge in the practical field. 

This element represents a future strength that will lead to a more in-depth analysis considering new 

methodologies. For example, future investigations could include the targeted analysis of individual case 

studies.  

Moreover, at the level of business models, it would be interesting to analyse how the perception of Tech 

companies has changed compared to the previous and current period of COVID-19.   
 

Table 3. Elements under discussion and case studies analysed for each topic 



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Macro variables  Case studies  
• Drugs discovery and 

knowledge sharing 
• Tracking and prediction 
• Clinical decision-making and 

diagnosis 
• Social distancing control and 

medical chatbots 

DeepMind; Insilico Medicine 
 

 

BlueDot 
Infervision; Alibaba 
 
Microsoft Healthcare Bot 

Source: Authors’ elaboration 
  



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