Beyond regulatory compliance: evaluating radiology artificial intelligence applications in deployment
Jack Ross, Salah Hammouche, Joseph C. Y. Chen, Andrea Rockall, Samer Alabed, Mitchell Chen, Krit Dwivedi, Dan Fascia, Rebecca Greenhalgh, Mark Hall, K. Halliday, Stephen Harden, William Ramsden, Susan C. Shelmerdine
Abstract
The implementation of artificial intelligence (AI) applications in routine practice, following regulatory approval, is currently limited by practical concerns around reliability, accountability, trust, safety, and governance, in addition to factors such as cost-effectiveness and institutional information technology support. When a technology is new and relatively untested in a field, professional confidence is lacking and there is a sense of the need to go above the baseline level of validation and compliance. In this article, we propose an approach that goes beyond standard regulatory compliance for AI apps that are approved for marketing, including independent benchmarking in the lab as well as clinical audit in practice, with the aims of increasing trust and preventing harm.