A deployment safety case for AI-assisted prostate cancer diagnosis.

Abstract

Deep learning (DL) has the potential to deliver significant clinical benefits. In recent years, an increasing number of DL-based systems have been approved by the relevant regulators, e.g. FDA. Although obtaining regulatory approvals is a prerequisite to deploy such systems for real world use, it may not be sufficient. Regulatory approvals give confidence in the development process for such systems, but new hazardous events can arise depending on how the systems have been deployed in the intende