  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.
