Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data
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.
Main Authors
Format
Conferences
Conference paper
Published
2021
Subjects
Publication in research information system
Publisher
IEEE
The permanent address of the publication
https://urn.fi/URN:NBN:fi:jyu-202201261285Käytä tätä linkitykseen.
Parent publication ISBN
978-1-6654-0126-5
Review status
Peer reviewed
DOI
https://doi.org/10.1109/bibm52615.2021.9669525
Conference
IEEE International Conference on Bioinformatics and Biomedicine
Language
English
Is part of publication
IEEE BIBM 2021 : Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine, December 9-12, 2021, Virtual Event
Citation
- Kalliokoski, T. (2021). Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data. In Y. Huang, L. Kurgan, F. Luo, X. Hu, Y. Chen, E. Dougherty, A. Kloczkowski, & Y. Li (Eds.), IEEE BIBM 2021 : Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine, December 9-12, 2021, Virtual Event (pp. 3157-3164). IEEE. https://doi.org/10.1109/bibm52615.2021.9669525
License
Copyright© 2021 IEEE