Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead

Adadi, Amina; Lahmer, Mohammed; Nasiri, Samia

doi:10.1016/j.jksuci.2021.07.010

Cited by 30 publications

(25 citation statements)

References 83 publications

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“…Literature documenting the impact of Artificial intelligence on COVID-19 is growing and encouraging. For instance, Senthilraja [6], Adadi et al [24], and Islam et al [5], among many other authors, documented how AI has been applied in health, particularly in addressing the impact of COVID-19. However, in the literature reviewed, there is consistency in the application of AI in health, particularly in addressing the negative implications of COVID-19.…”

Section: Brief Review Of Literaturementioning

confidence: 99%

Section: Brief Review Of Literaturementioning

confidence: 99%

“…Another study by Adadi et al [24] revealed that AI played a huge role in the response to the COVID-19 pandemic. Adadi et al [24] stated that the growing interest in using AI in handling COVID-19 issues has led to an increase in AI research, leading to a rise in the number of articles and review studies in a short space of time. A study by Islam et al [5] also highlighted that the application of AI in health has been on the rise, and the rate has been increased exponentially by the COVID-19 pandemic.…”

Section: Brief Review Of Literaturementioning

confidence: 99%

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The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals

Mhlanga

2022

IJERPH

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The COVID-19 pandemic came with disruptions in every aspect of human existence, with all the sectors of the economies of the world affected greatly. In the health sector, the pandemic halted and reversed progress in health and subsequently shortened life expectancy, especially in developing and underdeveloped nations. On the other hand, machine learning and artificial intelligence contributed a great deal to the handling of the pandemic globally. Therefore, the current study aimed to assess the role played by artificial intelligence and machine learning in addressing the dangers posed by the COVID-19 pandemic, as well as extrapolate the lessons on the fourth industrial revolution and sustainable development goals. Using qualitative content analysis, the results indicated that artificial intelligence and machine learning played an important role in the response to the challenges posed by the COVID-19 pandemic. Artificial intelligence, machine learning, and various digital communication tools through telehealth performed meaningful roles in scaling customer communications, provided a platform for understanding how COVID-19 spreads, and sped up research and treatment of COVID-19, among other notable achievements. The lessons we draw from this is that, despite the disruptions and the rise in the number of unintended consequences of technology in the fourth industrial revolution, the role played by artificial intelligence and machine learning motivates us to conclude that governments must build trust in these technologies, to address health problems going forward, to ensure that the sustainable development goals related to good health and wellbeing are achieved.

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Section: Brief Review Of Literaturementioning

confidence: 99%

Section: Brief Review Of Literaturementioning

confidence: 99%

Section: Brief Review Of Literaturementioning

confidence: 99%

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The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals

Mhlanga

2022

IJERPH

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“…Although the authors focused on diagnostics, they poorly reported on accuracy measures and explicit description of the extraction process. None of the included studies performed a meta-analysis, similar to what Adadi et al [ 86 ] found in their study.…”

Section: Discussionmentioning

confidence: 53%

Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

Jemioło

Storman

Orzechowski

2022

JCM

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The COVID-19 pandemic has sparked a barrage of primary research and reviews. We investigated the publishing process, time and resource wasting, and assessed the methodological quality of the reviews on artificial intelligence techniques to diagnose COVID-19 in medical images. We searched nine databases from inception until 1 September 2020. Two independent reviewers did all steps of identification, extraction, and methodological credibility assessment of records. Out of 725 records, 22 reviews analysing 165 primary studies met the inclusion criteria. This review covers 174,277 participants in total, including 19,170 diagnosed with COVID-19. The methodological credibility of all eligible studies was rated as critically low: 95% of papers had significant flaws in reporting quality. On average, 7.24 (range: 0–45) new papers were included in each subsequent review, and 14% of studies did not include any new paper into consideration. Almost three-quarters of the studies included less than 10% of available studies. More than half of the reviews did not comment on the previously published reviews at all. Much wasting time and resources could be avoided if referring to previous reviews and following methodological guidelines. Such information chaos is alarming. It is high time to draw conclusions from what we experienced and prepare for future pandemics.

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“…The COVID-19 pandemic has been around for nearly two years, and it is time to prepare for the post-Covid-19, as artificial intelligence (AI) is being relied upon, which has actively contributed to containing the global health crisis. (Adadi, A., Lahmer, M., & Nasiri, S., 2021). Dobson-Lohman, E., and Potcovaru, A.-M. (2020) conducted analysis and provided estimates regarding the relationship between COVID-19 anxiety, emotional contagion, and responsible media reporting.…”

Section: Introductionmentioning

confidence: 99%

A Bibliometric Analysis of Artificial Intelligence applications during COVID-19 Based on Web of Science (WoS) Database

الغامدي¹

2022

Journal of Engineering Sciences and Information Technology

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This article opens up another analytic method of Artificial Intelligence applications in Light of COVID-19, mainly explaining this binding domain's current trends and knowledge areas, according to the data analysis of previous studies in this field. The bibliometric study was performed to present new research trends in Artificial Intelligence in light of COVID-19. The data of 1635 studies published in Web of Science were analyzed during the last two years (2020-2021). We achieved the bibliometric analysis using three software CiteSpace, VOSviewer, and KnowledgeMatrix Plus. The findings of bibliometric analysis suggest that there are twelve research clusters in this topic (emerging industry, cross-sectional survey study, emerging technologies, joint position paper, colony predation algorithm, medical worker, deep learning, covid-19 risk prediction, future smart connected communities, supply chain resilience, virtual screening, and k-12 students). The United States, People's Republic of China, the United Kingdom, India, Saudi Arabia, Italy, Australia, Spain, South Korea, and Canada are the most intriguing countries that investigated this issue during COVID-19, so this study reveals the latest policy trends in Artificial intelligence using bibliometric analysis.

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Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead

Cited by 30 publications

References 83 publications

The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals

The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals

Artificial Intelligence for COVID-19 Detection in Medical Imaging—Diagnostic Measures and Wasting—A Systematic Umbrella Review

A Bibliometric Analysis of Artificial Intelligence applications during COVID-19 Based on Web of Science (WoS) Database

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