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

TL;DR

This paper reviews existing AI models for COVID-19 diagnosis from chest X-ray data, emphasizing the need for better documentation, bias analysis, and explainability for clinical usability.

Contribution

It provides a comprehensive set of requirements for future AI models to be clinically applicable in COVID-19 diagnosis from chest X-ray images.

Findings

AI models need thorough documentation

Bias and performance analysis are essential

Explainability modules are crucial for clinical use

Abstract

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.