1. Radiology Implementation Considerations for Artificial Intelligence (AI) Applied to COVID-19, From the AJR Special Series on AI Applications
- Author
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Brent P. Little, Jayashree Kalpathy-Cramer, Matthew D. Li, Michael H. Chung, Xueyan Mei, Sharon Steinberger, Ken Chang, and Adam Bernheim
- Subjects
medicine.medical_specialty ,business.industry ,General Medicine ,Institutional review board ,Field (computer science) ,Systematic review ,Software deployment ,Medical imaging ,Medicine ,Radiology, Nuclear Medicine and imaging ,Use case ,Applications of artificial intelligence ,Artificial intelligence ,Radiology ,User interface ,business - 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.
- Published
- 2022