# Mapping the Landscape of Artificial Intelligence Applications against COVID-19

arXiv: https://arxiv.org/abs/2003.11336 · HTML: https://mlsanity.com/paper/2003.11336/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 14 of 14)

- [Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer](https://mlsanity.com/paper/2207.00497/) (2022)
- [Forecasting new diseases in low-data settings using transfer learning](https://mlsanity.com/paper/2204.05059/) (2022)
- [Transferring Domain Knowledge with (X)AI-Based Learning Systems](https://mlsanity.com/paper/2406.01329/) (2024)
- [Semi-supervised Learning for COVID-19 Image Classification via ResNet](https://mlsanity.com/paper/2103.06140/) (2021)
- [Some Pragmatic Prevention's Guidelines regarding SARS-CoV-2 and COVID-19 in Latin-America inspired by mixed Machine Learning Techniques and Artificial Mathematical Intelligence. Case Study: Colombia](https://mlsanity.com/paper/2105.12213/) (2021)
- [Neural networks for dengue forecasting: a systematic review](https://mlsanity.com/paper/2106.12905/) (2021)
- [Comparative Evaluation of Radiomics and Deep Learning Models for Disease Detection in Chest Radiography](https://mlsanity.com/paper/2504.12249/) (2025)
- [Did Chatbots Miss Their 'Apollo Moment'? A Survey of the Potential, Gaps and Lessons from Using Collaboration Assistants During COVID-19](https://mlsanity.com/paper/2103.05561/) (2021)
- [Understanding Human Innate Immune System Dependencies using Graph Neural Networks](https://mlsanity.com/paper/2108.02872/) (2021)
- [Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19](https://mlsanity.com/paper/2108.10791/) (2021)
- [OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings](https://mlsanity.com/paper/2109.04004/) (2021)
- [Internationalizing AI: Evolution and Impact of Distance Factors](https://mlsanity.com/paper/2112.01231/) (2021)
- [Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images](https://mlsanity.com/paper/2401.07020/) (2024)
- [From Lab to Pocket: A Novel Continual Learning-based Mobile Application for Screening COVID-19](https://mlsanity.com/paper/2410.12589/) (2024)

## Abstract (arXiv)

COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, which has reported over 18 million confirmed cases as of August 5, 2020. In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis. We have identified applications that address challenges posed by COVID-19 at different scales, including: molecular, by identifying new or existing drugs for treatment; clinical, by supporting diagnosis and evaluating prognosis based on medical imaging and non-invasive measures; and societal, by tracking both the epidemic and the accompanying infodemic using multiple data sources. We also review datasets, tools, and resources needed to facilitate Artificial Intelligence research, and discuss strategic considerations related to the operational implementation of multidisciplinary partnerships and open science. We highlight the need for international cooperation to maximize the potential of AI in this and future pandemics.