A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading

Jamshidi, Mohammad; Roshani, Sobhan; Talla, Jakub; Lalbakhsh, Ali; Peroutka, Zdeněk; Roshani, Sobhan; Parandin, Fariborz; Malek, Zahra; Daneshfar, Fatemeh; Niazkar, Hamid Reza; Lotfi, Saeedeh; Sabet, Asal; Dehghani, Mojgan; Hadjilooei, Farimah; Sharifi-Atashgah, Maryam S.; Lalbakhsh, Pedram

doi:10.3390/ai3020028

Cited by 19 publications

(10 citation statements)

References 76 publications

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“…Secondly, the Naranjo score also provides a better idea on the degree of association by generating data on a numerical scale which allows for in-depth analysis. Centers should continue reporting such occurrences of COVID-19-induced pancreatitis and consider incorporating our modified Naranjo score; artificial intelligence methods may subsequently be used to diagnose COVID-19-induced pancreatitis[ 101 , 102 ].…”

Section: Discussionmentioning

confidence: 99%

Occam’s razor or Hickam’s dictum-COVID-19 is not a textbook aetiology of acute pancreatitis: A modified Naranjo Score appraisal

Teng¹,

Chua²,

Lim³

et al. 2023

World J Gastroenterol

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BACKGROUND Acute pancreatitis (AP) is a disease spectrum ranging from mild to severe disease. During the coronavirus disease 2019 (COVID-19) pandemic, numerous reports of AP have been published, with most authors concluding a causal relationship between COVID-19 and AP. Retrospective case reports or small case series are unable to accurately determine the cause-effect relationship between COVID-19 and AP. AIM To establish whether COVID-19 is a cause of AP using the modified Naranjo scoring system. METHODS A systematic review was conducted on PubMed, World of Science and Embase for articles reporting COVID-19 and AP from inception to August 2021. Exclusion criteria were cases of AP which were not reported to be due to COVID-19 infection, age < 18 years old, review articles and retrospective cohort studies. The original 10-item Naranjo scoring system (total score 13) was devised to approximate the likelihood of a clinical presentation to be secondary to an adverse drug reaction. We modified the original scoring system into a 8-item modified Naranjo scoring system (total score 9) to determine the cause-effect relationship between COVID-19 and AP. A cumulative score was decided for each case presented in the included articles. Interpretation of the modified Naranjo scoring system is as follows: ≤ 3: Doubtful, 4-6: Possible, ≥ 7: Probable cause. RESULTS The initial search resulted in 909 articles, with 740 articles after removal of duplicates. A total of 67 articles were included in the final analysis, with 76 patients which had AP reported to be due to COVID-19. The mean age was 47.8 (range 18-94) years. Majority of patients (73.3%) had ≤ 7 d between onset of COVID-19 infection and diagnosis of AP. There were only 45 (59.2%) patients who had adequate investigations to rule out common aetiologies (gallstones, choledocholithiasis, alcohol, hypertriglyceridemia, hypercalcemia and trauma) of AP. Immunoglobulin G4 testing was conducted in 9 (13.5%) patients to rule out autoimmune AP. Only 5 (6.6%) patients underwent endoscopic ultrasound and/or magnetic resonance cholangiopancreatogram to rule out occult microlithiasis, pancreatic malignancy and pancreas divisum. None of the patients had other recently diagnosed viral infections apart from COVID-19 infection, or underwent genetic testing to rule out hereditary AP. There were 32 (42.1%), 39 (51.3%) and 5 (6.6%) patients with doubtful, possible, and probable cause-effect relationship respectively between COVID-19 and AP. CONCLUSION Current evidence is weak to establish a strong link between COVID-19 and AP. Investigations should be performed to rule out other causes of AP before establishing COVID-19 as an aetiology.

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Section: Discussionmentioning

confidence: 99%

Occam’s razor or Hickam’s dictum-COVID-19 is not a textbook aetiology of acute pancreatitis: A modified Naranjo Score appraisal

Teng¹,

Chua²,

Lim³

et al. 2023

World J Gastroenterol

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“… 61 COVID-19 epidemic data have been analysed recently by researchers, using AI diagnostic tools. 62 , 63 Note that EVT is asymptotic and 1DOF, while this study introduces MDOF and sub-asymptotic approaches. To summarize, the predicted non-dimensional λ level, indicated by the star in Figure 6 , represents the probability of world cancer deaths in the years to come.…”

Section: Resultsmentioning

confidence: 99%

COVID-19 Epidemic Forecast in Brazil

Gaidai

Xing

2023

Bioinform Biol Insights

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This study advocates a novel spatio-temporal method for accurate prediction of COVID-19 epidemic occurrence probability at any time in any Brazil state of interest, and raw clinical observational data have been used. This article describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient time period, resulting in robust long-term forecast of the virus outbreak probability. COVID-19 daily numbers of recorded patients in all affected Brazil states were taken into account. This work aimed to benchmark novel state-of-the-art methods, making it possible to analyse dynamically observed patient numbers while taking into account relevant regional mapping. Advocated approach may help to monitor and predict possible future epidemic outbreaks within a large variety of multi-regional biological systems. Suggested methodology may be used in various modern public health applications, efficiently using their clinical survey data.

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“…The use of artificial intelligence (AI) in clinical care and public health contexts has expanded rapidly in recent years [1][2][3][4][5][6], including throughout the COVID-19 pandemic [7][8][9][10][11][12][13][14][15]. While emerging AI applications have the potential to improve health care quality and fairness [16][17][18][19][20][21], they may alternatively perpetuate or exacerbate inequities if they are not designed, deployed, and monitored appropriately [22][23][24][25][26].…”

Section: Background and Rationalementioning

confidence: 99%

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

Berdahl¹,

Baker²,

Mann³

et al. 2023

JMIR AI

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Background Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. Objective This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. Methods We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. Results In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. Conclusions Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.

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A Review of the Potential of Artificial Intelligence Approaches to Forecasting COVID-19 Spreading

Cited by 19 publications

References 76 publications

Occam’s razor or Hickam’s dictum-COVID-19 is not a textbook aetiology of acute pancreatitis: A modified Naranjo Score appraisal

Occam’s razor or Hickam’s dictum-COVID-19 is not a textbook aetiology of acute pancreatitis: A modified Naranjo Score appraisal

COVID-19 Epidemic Forecast in Brazil

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

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