Researchers introduced Generalist-Specialist-Mixture-of-Experts (GS-MoE) architecture for multimodal medical imaging, balancing modality-specific specialization with cross-modal shared representations. GS-MoE performed well on a dataset of 1.35M images, recovering detection of rare pathologies with per-class gains up to +0.60 F1. This may inform the development of AI agents for medical imaging tasks.