Requirement analysis for an artificial intelligence model for the diagnosis of the COVID-19 from chest X-ray data
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
© 2021 IEEE
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.
Parent publication ISBN978-1-6654-0126-5
ConferenceIEEE International Conference on Bioinformatics and Biomedicine
Is part of publicationIEEE BIBM 2021 : Proceedings of the 2021 IEEE International Conference on Bioinformatics and Biomedicine, December 9-12, 2021, Virtual Event
Publication in research information system
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