Challenges and Solutions in Deploying Systematized Nomenclature of Medicine—Clinical Terms in the Chinese Healthcare Context

ABSTRACT Systematized nomenclature of medicine—clinical terms (SNOMED CT), one of the most comprehensive clinical terminology systems, is pivotal in enhancing healthcare interoperability, clinical data governance, and medical artificial intelligence (AI) development globally. In China, with the rapid growth of large‐scale models and an increasing emphasis on transforming the intrinsic value of healthcare data, the absence of a nationally unified clinical terminology standard poses significant challenges. This commentary provides an in‐depth analysis of the benefits of SNOMED CT for global healthcare, examines the critical deficiencies in Chinese healthcare big data and AI development due to the lack of standardized terminology, and outlines the technical, administrative, and educational challenges encountered in deploying SNOMED CT within Chinese environments. Special emphasis is laid on the potential of advanced large language models in facilitating the mapping of Chinese clinical data to SNOMED CT. We further discuss the necessity of high‐quality data standardization in advancing medical AI in China. Finally, key conclusions and a roadmap for overcoming these challenges are proposed.