Reducing Clinician Annotation Fatigue in Open-Set Federated Learning: A Client-Adaptive Vision-Language Gatekeeper

Purpose Federated learning enables breast-imaging sites to jointly train mammography artificial intelligence (AI) without sharing images, but radiologists at each site must still annotate selected images during active-learning rounds. We developed and evaluated a client-adaptive vision-language gatekeeper that withholds radiopaque artifact-containing images considered unsuitable for a breast-density annotation objective and quantified its effect on annotation time, queue quality, and label reliability. Methods In this retrospective, scanner-partitioned study, we used FedEMBED, derived from the public Emory Breast Imaging Dataset (222,700 training and 65,891 test images; four scanners; artifact prevalence 4.8%-13.1% per scanner). PromptGate adapted a biomedical vision-language model with site-specific learnable prompts and classified each image as relevant in-distribution (ID) or irrelevant out-of-distribution …