# The Prominence of Artificial Intelligence in COVID-19

arXiv: https://arxiv.org/abs/2111.09537 · HTML: https://mlsanity.com/paper/2111.09537/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 180 references that are in the map.
- First posted on arXiv 18 Nov 2021.

## Builds on (most cited first, 30 of 33)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [COVID-19 Image Data Collection: Prospective Predictions Are the Future](https://mlsanity.com/paper/2006.11988/) (2020)
- [COVID-CT-Dataset: A CT Scan Dataset about COVID-19](https://mlsanity.com/paper/2003.13865/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [Deep Learning COVID-19 Features on CXR using Limited Training Data Sets](https://mlsanity.com/paper/2004.05758/) (2020)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images](https://mlsanity.com/paper/2003.11055/) (2020)
- [Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection](https://mlsanity.com/paper/2003.10769/) (2020)
- [Rapid AI Development Cycle for the Coronavirus (COVID-19) Pandemic: Initial Results for Automated Detection & Patient Monitoring using Deep Learning CT Image Analysis](https://mlsanity.com/paper/2003.05037/) (2020)
- [COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images](https://mlsanity.com/paper/2004.02696/) (2020)
- [COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs](https://mlsanity.com/paper/2003.14395/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach](https://mlsanity.com/paper/2004.10641/) (2020)
- [Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset](https://mlsanity.com/paper/2004.02060/) (2020)
- [COVID_MTNet: COVID-19 Detection with Multi-Task Deep Learning Approaches](https://mlsanity.com/paper/2004.03747/) (2020)
- [Coronavirus (COVID-19) Classification using CT Images by Machine Learning Methods](https://mlsanity.com/paper/2003.09424/) (2020)
- [Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT](https://mlsanity.com/paper/2005.03264/) (2020)
- [COVID-FACT: A Fully-Automated Capsule Network-based Framework for Identification of COVID-19 Cases from Chest CT scans](https://mlsanity.com/paper/2010.16041/) (2020)
- [Fake News Detection in Social Media using Graph Neural Networks and NLP Techniques: A COVID-19 Use-case](https://mlsanity.com/paper/2012.07517/) (2020)
- [DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting](https://mlsanity.com/paper/2102.06684/) (2021)
- [Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset](https://mlsanity.com/paper/2004.01184/) (2020)
- [Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil](https://mlsanity.com/paper/2007.12261/) (2020)
- [Odyssey: Creation, Analysis and Detection of Trojan Models](https://mlsanity.com/paper/2007.08142/) (2020)
- [Automated Detection of COVID-19 from CT Scans Using Convolutional Neural Networks](https://mlsanity.com/paper/2006.13212/) (2020)
- [Exploration and Discovery of the COVID-19 Literature through Semantic Visualization](https://mlsanity.com/paper/2007.01800/) (2020)
- [SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network](https://mlsanity.com/paper/2107.01330/) (2021)

## Built on by (most cited first, 7 of 7)

- [Brain Tumor Segmentation using Enhanced U-Net Model with Empirical Analysis](https://mlsanity.com/paper/2210.13336/) (2022)
- [Generative Adversarial Networks for Data Augmentation](https://mlsanity.com/paper/2306.02019/) (2023)
- [Introduction to Medical Imaging Informatics](https://mlsanity.com/paper/2306.00421/) (2023)
- [Case Studies on X-Ray Imaging, MRI and Nuclear Imaging](https://mlsanity.com/paper/2306.02055/) (2023)
- [AutoML Systems For Medical Imaging](https://mlsanity.com/paper/2306.04750/) (2023)
- [Online learning for X-ray, CT or MRI](https://mlsanity.com/paper/2306.06491/) (2023)
- [Invariant Scattering Transform for Medical Imaging](https://mlsanity.com/paper/2307.04771/) (2023)

## Abstract (arXiv)

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies proposed that can aid doctors and researchers in early and inexpensive methods of diagnosis of the disease. Most developing countries have difficulties carrying out tests using the conventional manner, but a significant way can be adopted with Machine and Deep Learning. On the other hand, the access to different types of medical images has motivated the researchers. As a result, a mammoth number of techniques are proposed. This paper first details the background knowledge of the conventional methods in the Artificial Intelligence domain. Following that, we gather the commonly used datasets and their use cases to date. In addition, we also show the percentage of researchers adopting Machine Learning over Deep Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the research challenges, we elaborate on the problems faced in COVID-19 research, and we address the issues with our understanding to build a bright and healthy environment.