Discussion

This review study was carried out in 2022 with the aim of assessing the functionalities of AI in the COVID-19 pandemic. The studies of AI functionalities in the COVID-19 pandemic depict satisfactory experiences of health-care staff, improved clinical workflow, effective role of such technologies in clinical decision-making, high-risk patient prioritization, improved hospital resource allocation, decreased laboratory work-up expenses, increased diagnostic precision, community safety, and efficient control and management of the pandemic. ^{Reference Assaf and Gutman Ya1,Reference Aslam6,Reference Carlile, Hurt and Hsiao10,Reference Laguarta, Hueto and Subirana18} Researchers and health-care staff have looked for new technologies to minimize the damage caused by the virus in this pandemic. Proper health care can take great advantage of technologies such as AI to combat new diseases and have a prospective approach. AI can help overcome the pandemic through screening the general public, medical assistance, infection prevention and control alerts and recommendations, surveillance of the pandemic prevalence, and making informed choices. Recent studies have shown the successful application of AI in health care. This technology is potentially capable of planning and improving the treatment course and outcome and is considered an evidence-based medical tool. However, to fully realize the complete potential of this technology in this pandemic requires more time and research. ^{Reference Vaishya, Javaid and Khan19–Reference Khan, Mehran and Haq21}

COVID-19 has posed many challenges to patients and health-care systems. This virus has the highest transmission rate compared with other viruses and is fatal to the elderly or some patients with background disorders. Even the countries with the fittest health-care systems have encountered ground-shaking challenges with the most basic accommodations. ^{Reference Hussain, Mirza and Hassan22} So, it would be crucial to apply appropriate strategies to impede COVID-19. The existing evidence has shown the advantages of classic approaches for care delivery. However, logistic barriers, economical factors, lack of enough staff and hospital equipment, general circumstances of the pandemic, and lock-downs have disrupted on-time and sufficient care delivery to the critical COVID-19 patients, especially in low- and middle-income countries. New approaches are recommended in such circumstances. ^{Reference Velayati, Ayatollahi and Hemmat23–Reference Kalhori, Bahaadinbeigy and Deldar26} AI is a relatively new appliance that can swiftly detect COVID-19, help diagnose, monitor treatment, trace physical encounters, predict mortality, help develop new medications and vaccines, and reduce health-care staff burnout. ^{Reference Vaishya, Javaid and Khan19} In fact, AI techniques are used to analyze the data on disasters to support better management. ^{Reference Sun, Bocchini and Davison27}

The studies were performed in the United States, ^{Reference Carlile, Hurt and Hsiao10,Reference Laguarta, Hueto and Subirana18} Israel, ^{Reference Assaf and Gutman Ya1} and Saudi Arabia. ^{Reference Aslam6} Although novel technologies such as AI have been more welcomed in high-income countries in light of more access to electronic instruments, substantial information technology infrastructure, higher computer literacy, and legislative support, ^{Reference Velayati, Ayatollahi and Hemmat23,Reference Keshvardoost, Bahaadinbeigy and Fatehi28} international collaboration has assisted its application in upper middle-income countries. Other factors have brought more attention to this technology in upper middle-income countries, including low access to practitioners, health-care service disparities, the low budget allocated to health care, weak coverage of health insurance, untrained health-care staff, transportation challenges, relatively low payrolls, low quality of life, limited opportunities for education, excessive work pressure, etc. ^{Reference Guo and Li29}

All studies were designed as cross-sectional to predict, detect, or diagnose COVID-19. ^{Reference Assaf and Gutman Ya1,Reference Aslam6,Reference Carlile, Hurt and Hsiao10,Reference Laguarta, Hueto and Subirana18} Two prospective cross-sectional studies in the United States (50%) used AI to predict or diagnose COVID-19. ^{Reference Carlile, Hurt and Hsiao10,Reference Laguarta, Hueto and Subirana18} AI was used in 2 retrospective cross-sectional studies in Israel and Saudi Arabia, which made up 50% of the studies. ^{Reference Assaf and Gutman Ya1,Reference Aslam6}

Cross-sectional studies evaluate the prevalence of health conditions or diseases and are straightforward, economical widely used. Only 1 cross-sectional study (25%) in 2020 in America used smartphone-based AI to process recorded voices of coughs. ^{Reference Laguarta, Hueto and Subirana18} Smartphone-based AI technologies permit cost-effective screening of medical conditions, health data analysis, efficient laboratory tests, timely diagnosis of diseases, improved treatment outcomes, analysis of vital signs, etc., using sensors and machine learning algorithms, microprocessors, and high-quality cameras. ^{Reference Shams, Hoque Apu and Rahman17,Reference Mantena, Celi and Keshavjee30} As Sheikh et al. have seen ^{Reference Sheikh, Bhatti and Adeyemi31} smartphone-based AI reached an accuracy of 89.5% and a specificity of 92.4% in diagnosing diabetic retinopathy. Two cross-sectional studies (50%) in 2020 in the United States and Saudi Arabia used Python, Keras, and TensorFlow to predict, detect, or diagnose COVID-19. These are among the most advanced and flexible open-source software that is used to train and test deep learning models. ^{Reference Aslam6,Reference Carlile, Hurt and Hsiao10}

The AI techniques used in these studies were deep learning and machine learning algorithms. Only 1 (25%) cross-sectional study in Israel used machine learning algorithms to triage critical COVID-19 patients (including neural network, random forest, and classification and regression decision tree). The results indicated that AI can be used as an efficient method to screen, optimize triage, deliver appropriate services to high-risk patients, and better manage the epidemic. ^{Reference Assaf and Gutman Ya1} Three cross-sectional studies (75%) used deep learning algorithms. Two (50%) of these studies used a convolutional neural network to predict asymptomatic patients and to diagnose COVID-19 pneumonia. The results of these studies showed the relatively high accuracy, sensitivity, and specificity of these algorithms compared with other classical diagnostic methods. ^{Reference Carlile, Hurt and Hsiao10,Reference Laguarta, Hueto and Subirana18} One study (25%) in Saudi Arabia used explainable AI to detect and predict mortality in COVID-19 patients. It showed an accuracy of more than 95% in clinical decision-making. ^{Reference Aslam6} Machine learning and deep learning techniques take up the relationship between the input and the output of complex processes. Therefore, they are used for decision-making and prediction, although it is not conceivable to interpret these models or justify the resulting prediction or decision. Machine learning algorithms have lower predictive capability compared with deep learning algorithms. Therefore, explainable AI techniques are used to deal with the challenges of machine learning and deep learning algorithms. It permits enhanced, more reliable, and more interpretable decision-making. ^{Reference Assaf and Gutman Ya1,Reference Aslam6,Reference Tripathy, Kabir and Arafat11}

There are numerous studies that have investigated the functionalities of AI for various diseases other than COVID-19. For instance, in a clinical trial by Wu et al. ^{Reference Wu, He and Liu32} an AI-based system (ENDOANGEl) could detect gastric cancer with an accuracy of 84.7%, a sensitivity of 100%, and a specificity of 84.3% in endoscopic imaging. In addition, a scoping review on AI in rehabilitation showed multiple applications, most commonly through robots and human-machine interaction. Most interventions were delivered in person to groups. ^{Reference Kaelin, Valizadeh and Salgado33} Providing AI infrastructure and access to proper databases are important steps for future studies, which should be addressed properly in the forthcoming research. To the best of the researchers’ knowledge, this was the first study to examine AI’s functionalities in the COVID-19 pandemic. The limitations of the present study were including articles only in English, excluding studies that were not found eligible based on the title and abstract, and the lack of access to the Embase database.

Therefore, it is recommended to further explore AI functionalities in pandemics and health care, including conducting clinical trial studies to investigate the effectiveness of this technology in the diagnosis and treatment of diseases, health policy-makers accepting and implementing these products, designing user-centered contents for AI applications, evaluating the readiness and acceptance of this technology in different countries, developing cost-effective AI products, providing sufficient infrastructure, improving computer literacy and educating the use of AI, and developing evidence-based educational contents for AI. Also, the introduction of AI will help the LMIC more using the smartphone technologies and better management strategies.