Artificial intelligence (AI) algorithms interpreting screening mammography exhibit diagnostic performance that can match or supersede human performance [1]. Unlike traditional medical products, AI is more fluid and can change overtime [2]. Although seen as a fundamental advantage of machine learning, this fluidity carries inherent risks. Prospective clinical trials are essential to understand the impact of AI on screening workflows before their implementation [3]. However, once implemented into …