A proposed artificial intelligence workflow to address application challenges leveraged on algorithm uncertainty.

ABSTRACT: Artificial 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

SUBMITTER: Li D 

PROVIDER: S-EPMC8924636 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature