Integrating AI‐Powered Digital Pathology With Case‐Based Teaching: A Novel Paradigm for Renal Education in Medical School

Medical students often struggle with understanding renal pathology due to its histological complexity and abstract clinical correlations. Traditional teaching approaches that rely on didactic lectures and static microscopy images frequently fail to engage learners or promote deep understanding. The emergence of digital pathology (DP) and artificial intelligence (AI) tools has opened new possibilities in medical education, especially in visual disciplines like pathology. Concurrently, case-based learning (CBL) and flipped classroom strategies are gaining traction for fostering active, clinically relevant learning. This perspective article proposes an integrated educational model that combines AI-powered DP with case-based teaching to enhance renal disease education for medical students. We discuss how AI-assisted whole slide imaging (WSI) platforms can support interactive exploration of renal lesions and simulate diagnostic reasoning. We also present a conceptual framework for a case-based flipped classroom (CBFC) approach that leverages annotated slides, clinical cases and active discussions. This hybrid model has the potential to improve student engagement, diagnostic accuracy and readiness for modern DP practice while also aligning with competency-based medical education principles. We outline benefits, implementation considerations and future directions for research and curriculum design.