How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future

Summary The clinical severity, rapid transmission and human losses due to coronavirus disease 2019 (Covid‐19) have led the World Health Organization to declare it a pandemic. Traditional epidemiological tools are being significantly complemented by recent innovations especially using artificial intelligence (AI) and machine learning. AI‐based model systems could improve pattern recognition of disease spread in populations and predictions of outbreaks in different geographical locations. A variable and a minimal amount of data are available for the signs and symptoms of Covid‐19, allowing a composite of maximum likelihood algorithms to be employed to enhance the accuracy of disease diagnosis and to identify potential drugs. AI‐based forecasting and predictions are expected to complement traditional approaches by helping public health officials to select better response and preparedness measures against Covid‐19 cases. AI‐based approaches have helped address the key issues but a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid‐19 pandemic.

a significant impact on the global healthcare industry is yet to be achieved. The capability of AI to address the challenges may make it a key player in the operation of healthcare systems in future. Here, we present an overview of the prospective applications of the AI model systems in healthcare settings during the ongoing Covid-19 pandemic. Currently, progress in information and communication technologies has led to an enormous increase in the amount of data obtained from public health surveillance. AI-based tools along with reliable disease management platforms have the potential to create an avenue for a robust analysis, enabling stakeholders to respond effectively to an infectious disease outbreak. 8,9 Previously, confirming the diagnosis in tuberculosis was a tedious and time-consuming process that hampered its control globally.
Currently, an early detection system has been achieved to a fair extent using an AI-based tool, artificial immune recognition system (ARIS). Likewise, ARIS has paved the way for AI-assisted diagnosis in several other difficult infections. 10 Another example of AI utilization in human healthcare is its successful implementation in the accurate diagnosis of malaria. It is a simple automated system that circumvents the complex processing and staining protocols, thus serves clinicians in minimizing potential errors. 11,12 AI-based technologies have also been effectively implemented in the epidemiology of infectious diseases such as Kyasanur forest disease, 13 Middle East respiratory syndrome (MERS), 14 Chikungunya, 15  accuracy. 23 The data generated on a daily basis through the internet of things along with classical datasets, can help us better understand the dynamics of infectious diseases, their progression, response to treatment and transmission if used cautiously.
AI-driven strategies in the crisis arising from infectious diseases are generally characterized by a poor quality of data and lack of immediate response. In such cases, composites of maximum likelihood approaches are used to address the issues such as small sample size or missing information. The proven maximum likelihood algorithms designed and tested for other infectious diseases following a similar set of natural history in the past can be very useful in predictions and decision-making. 18 This strategy worked well during severe acute respiratory syndrome (SARS) outbreak to ensure a quick response to public health needs. The previous experiences have acted as a major source of information during the decision-making for new global public health threat named Covid-19. 24 The Covid-19 smart management system utilized the data from sources such as credit cards, security camera records, global positioning system data from cars, or cell phones to effectively trace the movement of individuals with Covid-19 and their contacts in South Korea. 25 AIbased technologies are helping better understand the transmission pattern of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), assisting in development of novel diagnostics and effective therapeutic approaches, 26 proposing candidate drug molecules by screening millions of compounds, fostering vaccines in a time efficient and reliable manner. AI-based technologies also help to identify the F I G U R E 1 The scope of implementation of AI-based approaches in infectious diseases: (a) AI-based applications in infectious disease management including diagnosis, epidemiology, modelling, prediction, pathogen characterization, prevention, control and development of vaccines and therapeutics. (b) Control stages of infectious diseases from the emergence of infection in the environment until the implementation of the AI-based predictions and decisions by healthcare providers; AI, Artificial Intelligence; CT, computed tomography most susceptible individuals based on their personalized genetic, physiological and biochemical profiles. 27,28 Applications of AI-based approaches that could assist as key components of the Covid-19 pandemic are presented in Figure 1 and Table 1.
Covid-19 pandemic is a major global challenge 29,30 to public health along with devastating social, economic and political impacts. being screened for any eventual infections leading to their categorizations as low, moderate, or high risk individuals. [31][32][33] Recently, the AI and ML algorithm have been used to identify SARS-CoV-2 specific intrinsic genomic signatures for a rapid, scalable and highly accurate phylogenetic analysis of the virus. 33 During the course of the current pandemic, the AI tools may contribute significantly in better understanding the nature of the etiological virus (SARS-CoV-2), designing vaccines, identifying therapeutic targets, diagnosis of the disease and predicting new outbreaks spots based on the available digital data. 34

| Covid-19 datasets
The availability of sufficient amount of high-quality data is crucial for a successful implementation of AI in the management of Covid-19.
Several online resources have been created to provide a free online access to different types of data related to Covid-19 35

| AI in anti-SARS-CoV-2 drug design
Scientists across the world are working on the design and development of therapeutic molecules against the potential targets of SARS-CoV-2.
The main protease (M pro ) and RNA-dependent RNA polymerase (RdRp) are the most potent protein targets and inhibition of these viral proteins would substantially limit virus replication. Some of the AI-based approaches demonstrated their potential in identifying putative targets and proposed small molecule protease inhibitors. For instance, a recent study reported implementation of deep neural network-based models for de novo design of M pro protease inhibitors and identified thirty-one natural product based compounds. Two of these inhibitors displayed significant similarities with a plant product, aurantiamide, which may be used for Covid-19 treatment. 41 Another study reported some novel non-covalent inhibitors of M pro that were designed using generative deep learning approaches as potential drugs for treating Covid-19 patients. 35 Furthermore, a molecular docking study of 1903 approved drugs against M pro to select six drugs, viz. eszopiclone, perampanel, nelfinavir, pitavastatin, praziquantel and zopiclone, based on shape similarity analysis and docking scores. Nelfinavir was further selected as a potential drug candidate based on binding free energy calculation. 35 In another computational screening performed on clinically approved medicines, 36 ten drugs were identified to form hydrogen bonds with critical residues within the binding pocket of M pro protease of SARS-CoV-2 and may have a higher tolerance to resistance mutations. However, these AI based findings need to be validated using in vitro studies first and then in vivo studies to prove that the AI based binding is significant. Thus, AI could help select few molecules out of thousands to be further tested for developing into a therapeutic drug.
Following selection, the traditional drug development approaches need to be followed to determine the safety and efficacy.
One ML database, consisting of data on already known drugs with antiviral action against known viruses, coupled with a second one, containing known 3-chymotrypsin-like protease (3CLpro) inhibitors, predicted marketed drugs with potential for use against SARS-CoV-2. 37 In another study on drug repurposing, rapid which is sufficient to inhibit JAK-1, the drug is expected to resist viral entry and reduce inflammation in Covid-19 patients. 42 All such studies used AI or machine learning based methods directly or indirectly at different stages of computer aided drug designing.

| AI in SARS-CoV-2 vaccine design
The vaccine development strategies against the pathogenic human simultaneously elicit both humoural and cell-mediated immune responses. 40  CoV-2 and symptomatically related pathogens that could be used for diagnostic and surveillance purposes. 59 The CRISPR-Cas13 detection system was experimentally tested based on lateral-flow assay to demonstrate its speed and sensitivity using synthetic targets. 59 Therefore, AI approaches that can go hand-in-hand with the existing molecular diagnostic procedures could help in diagnosis and assist in early control of disease spread. AI-based tools can impart swiftness in the healthcare set up amidst the Covid-19 pandemic crisis through AI/ML algorithms, which are well-tested and verified in several disease outbreaks.

| AI in prediction of Covid-19 pandemic outcomes
Hitherto studies are conducted to increase the clinical skills for the identification and progression of Covid-19. 60   Further, a similar study evaluated the vulnerability and preparedness (based on IDVI) of African countries for Covid-19. 65  There is a need for AI, Cytomics or multi-OMICS based approaches for the development of rapid and easy to use assays to distinguish between severe and mild cases and to identify the patients expected to become critically ill. 76 This would take some of the pressure off clinicians, hospitals and public healthcare system to manage the Covid-19

| AI in determination of epidemiological trend
pandemic through optimal utilization of resources.

| Pitfalls of AI
The major limitations and challenges to success of AI-based approaches include the nature of disease manifestation. The SARS-CoV-2 infection has varied manifestations ranging from asymptomatic to severe clinical disease, which may require huge and complex data, and pose difficulty in designing practical AI-based algorithms.
Similar constraints were reported while developing algorithms of prediction using line classifiers for MERS in the past. 77,78 The digital systems used to gather the data are unlikely to achieve the required uptake using the voluntary system without any incentives; a smart phone based application generated data can be suppressed to an extent of 80% as observed in a simulation study. 79 Apart from that, a compromise between autonomy and privacy for an uncertain public health benefit can only be put into use after a warranted pilot project on modelling with sufficient benefit at the cost of privacy. The present AI-based algorithms in healthcare systems can offer a binary response to specific question about a disease in context, but may not give the alternative predictions, it would be hard and complicated to build such a comprehensive AI-based algorithm for health monitoring. 80  It is agreed that AI-based tools may not completely replace human brain in terms of observations made by virologists, epidemiologists or clinicians, however the value of AI will play a signifi-