Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data
This work addresses the gap in clinical applicability for AI-based COVID-19 diagnosis tools, but it is incremental as it synthesizes existing knowledge rather than proposing new methods.
The paper tackles the problem of insufficient clinical usability in existing AI models for COVID-19 diagnosis from chest X-rays by analyzing reviews and guidelines to generate comprehensive requirements, resulting in key findings that emphasize the need for good documentation, statistical analysis, and explainability.
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.