# Post-COVID Highlights: Challenges and Solutions of AI Techniques for Swift Identification of COVID-19

arXiv: https://arxiv.org/abs/2311.06258 · HTML: https://mlsanity.com/paper/2311.06258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 52 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans](https://mlsanity.com/paper/2008.06388/) (2020)
- [Deep-COVID: Predicting COVID-19 From Chest X-Ray Images Using Deep Transfer Learning](https://mlsanity.com/paper/2004.09363/) (2020)
- [JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation](https://mlsanity.com/paper/2004.07054/) (2020)
- [Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection](https://mlsanity.com/paper/2003.10769/) (2020)
- [Momentum Contrastive Learning for Few-Shot COVID-19 Diagnosis from Chest CT Images](https://mlsanity.com/paper/2006.13276/) (2020)
- [COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network](https://mlsanity.com/paper/2007.14638/) (2020)
- [Weakly Supervised Deep Learning for COVID-19 Infection Detection and Classification from CT Images](https://mlsanity.com/paper/2004.06689/) (2020)
- [An Uncertainty-aware Transfer Learning-based Framework for Covid-19 Diagnosis](https://mlsanity.com/paper/2007.14846/) (2020)
- [COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach](https://mlsanity.com/paper/2102.06285/) (2021)
- [Less is More: Unsupervised Mask-guided Annotated CT Image Synthesis with Minimum Manual Segmentations](https://mlsanity.com/paper/2303.12747/) (2023)
- [Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels](https://mlsanity.com/paper/2202.07422/) (2022)
- [CS2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention](https://mlsanity.com/paper/2206.13394/) (2022)
- [Few-shot Learning for CT Scan based COVID-19 Diagnosis](https://mlsanity.com/paper/2102.00596/) (2021)
- [Generalized Multi-Task Learning from Substantially Unlabeled Multi-Source Medical Image Data](https://mlsanity.com/paper/2110.13185/) (2021)
- [COVID Detection in Chest CTs: Improving the Baseline on COV19-CT-DB](https://mlsanity.com/paper/2107.04808/) (2021)
- [CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19](https://mlsanity.com/paper/2302.09004/) (2023)

## Abstract (arXiv)

Since the onset of the COVID-19 pandemic in 2019, there has been a concerted effort to develop cost-effective, non-invasive, and rapid AI-based tools. These tools were intended to alleviate the burden on healthcare systems, control the rapid spread of the virus, and enhance intervention outcomes, all in response to this unprecedented global crisis. As we transition into a post-COVID era, we retrospectively evaluate these proposed studies and offer a review of the techniques employed in AI diagnostic models, with a focus on the solutions proposed for different challenges. This review endeavors to provide insights into the diverse solutions designed to address the multifaceted challenges that arose during the pandemic. By doing so, we aim to prepare the AI community for the development of AI tools tailored to address public health emergencies effectively.