[Standardizing breast cancer digital pathology databases for artificial intelligence: practice and reflection]

The traditional paradigm of pathology diagnosis faces challenges like data fragmentation and inefficiency in the era of big data and artificial intelligence, necessitating a digital transformation built upon high-quality, standardized databases. This article reviews global efforts in digital pathology database construction and details our team's pilot practice in developing a specialized breast pathology database. We share our approach to case enrollment, systematic data collection, and standardized slide digitization. Key issues, including data schema design, multi-center integration, scanning protocols, and collaborative models, are discussed to inspire industry-wide standardization and support the sustainable development of digital pathology and artificial intelligence in China.