Artificial intelligence vs COVID-19: limitations, constraints and pitfalls

Naudé, Wim

doi:10.1007/s00146-020-00978-0

Cited by 298 publications

(224 citation statements)

References 20 publications

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“…The use of AI may require access to personal information to generate trends, make predictions, and conduct assessments. Moreover, individual patient information has been placed online on multiple platforms [ 48 ]. The sharing of such information may lead to the infringement of privacy and personal rights.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for COVID-19: Rapid Review

Chen¹,

See²

2020

J Med Internet Res

125

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Background COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. Objective To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. Methods We performed an extensive search of the PubMed and EMBASE databases for COVID-19–related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. Results In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. Conclusions In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence for COVID-19: Rapid Review

Chen¹,

See²

2020

J Med Internet Res

125

View full text Add to dashboard Cite

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“…A common weakness of any model is the lack of data. In the case of COVID-19, we can be con dent that this is the principal limitation in explaining the epidemic development in Mexico and elsewhere (Burdorf et al 2020;Naudé 2020). If we add the asymptomatic people not reported but still contagious (Bai et al 2020), the uncertainty may be higher.…”

Section: Remarks On Data Gatheredmentioning

confidence: 95%

Why does the COVID-19 outbreak in Mexico seem to be slower? Some hypotheses for testing

Elías-Gutiérrez

Mendoza‐Carranza

Takahashi-Aguilar³

et al. 2020

Preprint

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Aim: The current paper aims to present and discuss some hypothesis about why in Mexico and in particular the Mexico City the epidemy of COVID-19 seems to be slower than other countries. Subject and methods: We compared daily UVI maximum and the reported COVID cases in Mexico City, New York, and Los Angeles and performed an ADONIS permutation test to look for significant differences between case numbers and its relationship with the UV index. We performed a quantile regression to explain the relation between UVI and daily COVID cases. We also investigated and analyzed other possible factors contributing to this slowdown because UVI cannot be the only explanation. Results: Although Mexican authorities have applied one of the smaller numbers of tests in the world, it is possible to identify a slowdown in Mexico's rate of infections. We found a negative and significant difference between the UV and registered cases among the three cities analyzed. However, this slowdown cannot be explained by a single factor. It is possible to hypothesize about the confluence of other factors as Vitamin D levels in Mexican population, lack of central air conditioning and the ventilation in houses and metropolitan transportation systems. Also, the Bacillus Calmette–Guérin vaccination could be part of the explanation of this phenomenon. Conclusions: Single parameter models as solar irradiation can explain partially the complicated spread of COVID-19 in different parts of the world. However, it is necessary to extend our scientific understanding of environmental effects, city infrastructure, population age, vaccination schemes, socioeconomic conditions, social and political behavior on COVID-19 epidemic dynamics.

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“…In most of these studies, CT scans, biomarker profiles, and genome sequence data sets are limited to certain hospitals. 134 To apply AI in a clinical setting, the current regulatory and quality frameworks must be considered to enable AI-based decision making while respecting privacy laws. 131…”

Section: Emergence Of Ai For the Diagnosis And Prognosis Of Covid-19mentioning

confidence: 99%

Roadmap to the Bioanalytical Testing of COVID-19: From Sample Collection to Disease Surveillance

et al. 2020

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The disease caused by SARS-CoV-2, coronavirus disease 2019 (COVID-19), has led to a global pandemic with tremendous mortality, morbidity, and economic loss. The current lack of effective vaccines and treatments places tremendous value on widespread screening, early detection, and contact tracing of COVID-19 for controlling its spread and minimizing the resultant health and societal impact. Bioanalytical diagnostic technologies have played a critical role in the mitigation of the COVID-19 pandemic and will continue to be foundational in the prevention of the subsequent waves of this pandemic along with future infectious disease outbreaks. In this Review, we aim at presenting a roadmap to the bioanalytical testing of COVID-19, with a focus on the performance metrics as well as the limitations of various techniques. The state-of-the-art technologies, mostly limited to centralized laboratories, set the clinical metrics against which the emerging technologies are measured. Technologies for point-of-care and do-it-yourself testing are rapidly emerging, which open the route for testing in the community, at home, and at points-of-entry to widely screen and monitor individuals for enabling normal life despite of an infectious disease pandemic. The combination of different classes of diagnostic technologies (centralized and point-of-care and relying on multiple biomarkers) are needed for effective diagnosis, treatment selection, prognosis, patient monitoring, and epidemiological surveillance in the event of major pandemics such as COVID-19.

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Artificial intelligence vs COVID-19: limitations, constraints and pitfalls

Abstract: Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Cited by 298 publications

References 20 publications

Artificial Intelligence for COVID-19: Rapid Review

Artificial Intelligence for COVID-19: Rapid Review

Why does the COVID-19 outbreak in Mexico seem to be slower? Some hypotheses for testing

Roadmap to the Bioanalytical Testing of COVID-19: From Sample Collection to Disease Surveillance

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