A new perspective argues that medical imaging AI research should prioritize conceptual innovation—reframing problems, evaluation metrics, and clinical relevance—over algorithmic improvements alone. The article highlights that current academic incentives undervalue conceptual contributions, leading to misaligned objectives and limited real-world impact, and offers recommendations for researchers, mentors, and journals to better support such innovation.