# Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data

arXiv: https://arxiv.org/abs/2110.12464 · HTML: https://mlsanity.com/paper/2110.12464/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 100 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 19 of 19)

- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [Transfusion: Understanding Transfer Learning for Medical Imaging](https://mlsanity.com/paper/1902.07208/) (2019)
- [Learning Not to Learn: Training Deep Neural Networks with Biased Data](https://mlsanity.com/paper/1812.10352/) (2018)
- [Causality matters in medical imaging](https://mlsanity.com/paper/1912.08142/) (2019)
- [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)
- [What do we need to build explainable AI systems for the medical domain?](https://mlsanity.com/paper/1712.09923/) (2017)
- [Can we trust deep learning models diagnosis? The impact of domain shift in chest radiograph classification](https://mlsanity.com/paper/1909.01940/) (2019)
- [Deep Learning Predicts Hip Fracture using Confounding Patient and Healthcare Variables](https://mlsanity.com/paper/1811.03695/) (2018)
- [A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images](https://mlsanity.com/paper/2004.12823/) (2020)
- [Going Deep in Medical Image Analysis: Concepts, Methods, Challenges and Future Directions](https://mlsanity.com/paper/1902.05655/) (2019)
- [Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data](https://mlsanity.com/paper/2004.05405/) (2020)
- [Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection](https://mlsanity.com/paper/2012.11840/) (2020)
- [Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies](https://mlsanity.com/paper/2012.08333/) (2020)
- [Cats or CAT scans: transfer learning from natural or medical image source datasets?](https://mlsanity.com/paper/1810.05444/) (2018)
- [An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs](https://mlsanity.com/paper/2001.04051/) (2020)

## Abstract (arXiv)

There are multiple papers published about different AI models for the COVID-19 diagnosis with promising results. Unfortunately according to the reviews many of the papers do not reach the level of sophistication needed for a clinically usable model. In this paper I go through multiple review papers, guidelines, and other relevant material in order to generate more comprehensive requirements for the future papers proposing a AI based diagnosis of the COVID-19 from chest X-ray data (CXR). Main findings are that a clinically usable AI needs to have an extremely good documentation, comprehensive statistical analysis of the possible biases and performance, and an explainability module.