Radiology Implementation Considerations for Artificial Intelligence (AI) Applied to COVID-19, From the AJR Special Series on AI Applications

Matthew D. Li, Ken Chang, Xueyan Mei, Adam Bernheim, Michael Chung, Sharon Steinberger, Jayashree Kalpathy-Cramer, Brent P. Little

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

Abstract

Hundreds of imaging-based artificial intelligence (AI) models have been developed in response to the COVID-19 pandemic. AI systems that incorporate imaging have shown promise in primary detection, severity grading, and prognostication of outcomes in COVID-19, and have enabled integration of imaging with a broad range of additional clinical and epidemiologic data. However, systematic reviews of AI models applied to COVID-19 medical imaging have highlighted problems in the field, including methodologic issues and problems in real-world deployment. Clinical use of such models should be informed by both the promise and potential pitfalls of implementation. How does a practicing radiologist make sense of this complex topic, and what factors should be considered in the implementation of AI tools for imaging of COVID-19? This critical review aims to help the radiologist understand the nuances that impact the clinical deployment of AI for imaging of COVID-19. We review imaging use cases for AI models in COVID-19 (e.g., diagnosis, severity assessment, and prognostication) and explore considerations for AI model development and testing, deployment infrastructure, clinical user interfaces, quality control, and institutional review board and regulatory approvals, with a practical focus on what a radiologist should consider when implementing an AI tool for COVID-19.

Original languageEnglish
Pages (from-to)15-23
Number of pages9
JournalAmerican Journal of Roentgenology
Volume219
Issue number1
DOIs
StatePublished - Jul 2022

Keywords

  • COVID-19
  • artificial intelligence
  • deployment
  • implementation

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