文章基本信息

标题：A proposed artificial intelligence workflow to address application challenges leveraged on algorithm uncertainty
本地全文：下载
作者：Dantong Li ; Lianting Hu ; Xiaoting Peng 等
期刊名称：iScience
印刷版ISSN：2589-0042
出版年度：2022
卷号：25
期号：3
页码：1-20
DOI：10.1016/j.isci.2022.103961
语种：English
出版社：Elsevier
摘要：SummaryArtificial Intelligence (AI) has achieved state-of-the-art performance in medical imaging. However, most algorithms focused exclusively on improving the accuracy of classification while neglecting the major challenges in a real-world application. The opacity of algorithms prevents users from knowing when the algorithms might fail. And the natural gap between training datasets and the in-reality data may lead to unexpected AI system malfunction. Knowing the underlying uncertainty is essential for improving system reliability. Therefore, we developed a COVID-19 AI system, utilizing a Bayesian neural network to calculate uncertainties in classification and reliability intervals of datasets. Validated with four multi-region datasets simulating different scenarios, our approach was proved to be effective to suggest the system failing possibility and give the decision power to human experts in time. Leveraging on the complementary strengths of AI and health professionals, our present method has the potential to improve the practicability of AI systems in clinical application.Graphical abstractDisplay OmittedHighlights•A COVID-19 artificial intelligence diagnosis system with uncertainty estimation•Reliability and optional reliability intervals at dataset level as references•A proposed workflow that could be expanded to other diseases in practiceBioinformatics; Neural networks; Artificial intelligence