Artificial Intelligence in COVID-19 Management: A Systematic Review
Bibliographic record
Abstract
With the development of modern technologies in the field of healthcare, the use of Artificial Intelligence (AI) in disease management is increasing.AI methods may assist healthcare providers in the COVID-19 era.The current study aimed to observe the efficacy and importance of AI for managing the COVID-19 pandemic.An organized search was conducted, utilizing PubMed, Web of Science, Scopus, Embase, and Cochrane up to September 2022.Studies were considered qualified for inclusion if they met the inclusion criterion.We conducted review according to the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) guidelines.There were 52 documents that met the eligibility criteria to be included in the review.The most common item using AI during the COVID-19 era was predictive models to foretell pneumonia and mortality risks in people with COVID-19 based on medical and experimental parameters.COVID-19 mortality was related to being male and elderly based on the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) logistic regression analysis of demographics, clinical data, and laboratory tests of hospitalized COVID-19 patients.AI can predict, diagnose and model COVID-19 by using techniques such as support vector machines, decision trees, and neural networks.It is suggested that future research should deal with the design and development of AI-based tools for the management of chronic diseases such as COVID-19.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".