mlsanityData to 14 Oct 2026

Machine learning applications for COVID-19: A state-of-the-art review

arXiv 2101.07824 ↗First posted 19 Jan 2021cs.LGcited by 2

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Abstract (arXiv)

The COVID-19 pandemic has galvanized the machine learning community to create new solutions that can help in the fight against the virus. The body of literature related to applications of machine learning and artificial intelligence to COVID-19 is constantly growing. The goal of this article is to present the latest advances in machine learning research applied to COVID-19. We cover four major areas of research: forecasting, medical diagnostics, drug development, and contact tracing. We review and analyze the most successful state of the art studies. In contrast to other existing surveys on the subject, our article presents a high level overview of the current research that is sufficiently detailed to provide an informed insight.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 48 references that are in the map. First posted on arXiv 19 Jan 2021.

Builds on

11 of 48 references.

References · 11

  1. 2020Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networkscited by 92
  2. 2020Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networkscited by 86
  3. 2020Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networkscited by 64
  4. 2020Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19cited by 62
  5. 2020Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approachcited by 19
  6. 2020Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CTcited by 13
  7. 2020A Survey of COVID-19 Contact Tracing Appscited by 8
  8. 2020Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variablescited by 7
  9. 2019Forecasting significant stock price changes using neural networkscited by 4
  10. 2020Proximity Sensing: Modeling and Understanding Noisy RSSI-BLE Signals and Other Mobile Sensor Data for Digital Contact Tracingcited by 2
  11. 2020A Smartphone enabled Approach to Manage COVID-19 Lockdown and Economic Crisiscited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 1.

2 papers.

  1. 2021COVID-19 Modeling: A Reviewcited by 7
  2. 2022Review of Natural Language Processing in Pharmacologycited by 3