Researchers highlight an imbalance in medical imaging AI, where algorithmic progress outpaces conceptual innovation. They argue that underexamined foundations, such as task definitions and evaluation metrics, hinder meaningful clinical impact. Builders should consider the limitations of benchmark-driven progress and prioritize conceptual clarity. This imbalance affects the field's ability to deliver practical, clinically relevant solutions.