# Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies

arXiv: https://arxiv.org/abs/2012.08333 · HTML: https://mlsanity.com/paper/2012.08333/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 15 of 15)

- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [Towards Evaluating Explanations of Vision Transformers for Medical Imaging](https://mlsanity.com/paper/2304.06133/) (2023)
- [Multimodal Explainability via Latent Shift applied to COVID-19 stratification](https://mlsanity.com/paper/2212.14084/) (2022)
- [DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems](https://mlsanity.com/paper/2211.05764/) (2022)
- [Believe The HiPe: Hierarchical Perturbation for Fast, Robust, and Model-Agnostic Saliency Mapping](https://mlsanity.com/paper/2103.05108/) (2021)
- [Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review](https://mlsanity.com/paper/2112.12705/) (2021)
- [Aggregated Attributions for Explanatory Analysis of 3D Segmentation Models](https://mlsanity.com/paper/2407.16653/) (2024)
- [Glocal Explanations of Expected Goal Models in Soccer](https://mlsanity.com/paper/2308.15559/) (2023)
- [Model Science: getting serious about verification, explanation and control of AI systems](https://mlsanity.com/paper/2508.20040/) (2025)
- [LIMEcraft: Handcrafted superpixel selection and inspection for Visual eXplanations](https://mlsanity.com/paper/2111.08094/) (2021)
- [Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data](https://mlsanity.com/paper/2110.12464/) (2021)
- [Challenges facing the explainability of age prediction models: case study for two modalities](https://mlsanity.com/paper/2303.06640/) (2023)
- [Can Deep Learning Reliably Recognize Abnormality Patterns on Chest X-rays? A Multi-Reader Study Examining One Month of AI Implementation in Everyday Radiology Clinical Practice](https://mlsanity.com/paper/2305.10116/) (2023)
- [Prevention is better than cure: a case study of the abnormalities detection in the chest](https://mlsanity.com/paper/2305.10961/) (2023)
- [X-ray transferable polyrepresentation learning](https://mlsanity.com/paper/2507.06264/) (2025)

## Abstract (arXiv)

The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modules for explainability. In this work, we carry out a systematic analysis of various aspects of proposed models. Our analysis revealed numerous mistakes made at different stages of data acquisition, model development, and explanation construction. In this work, we overview the approaches proposed in the surveyed Machine Learning articles and indicate typical errors emerging from the lack of deep understanding of the radiography domain. We present the perspective of both: experts in the field - radiologists and deep learning engineers dealing with model explanations. The final result is a proposed checklist with the minimum conditions to be met by a reliable COVID-19 diagnostic model.