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.

Climate change poses a significant threat to global health. It exacerbates existing health challenges and generates new ones. Therefore, innovative solutions to mitigate and adapt to its adverse effects are urgently required. This article explores the potential of digital health technologies to address the challenge posed by climate change‐related health issues. It discusses their dual functionality of diminishing the carbon footprint of healthcare services and increasing understanding and governance of climate‐sensitive diseases. Notably, with advanced technologies such as Generative medical AI (GMAI) presenting environmental concerns like substantial energy consumption during data processing and the generation of electronic waste, it is essential to underscore the significance of their responsible development and implementation of these technologies. This will ensure that the benefits of digital health technologies can be maximized while minimizing their ecological drawbacks. This study, therefore propose, a framework for leveraging digital health technologies to support climate change adaptation, including disease surveillance, telemedicine, patient support systems, and public awareness campaigns.

Cancer informatics has significantly progressed in the big data era. We summarize the application of informatics approaches to the cancer domain from both the informatics perspective (e.g., data management and data science) and the clinical perspective (e.g., cancer screening, risk assessment, diagnosis, treatment, and prognosis). We discuss various informatics methods and tools that are widely applied in cancer research and practices, such as cancer databases, data standards, terminologies, high‐throughput omics data mining, machine‐learning algorithms, artificial intelligence imaging, and intelligent radiation. We also address the informatics challenges within the cancer field that pursue better treatment decisions and patient outcomes, and focus on how informatics can provide opportunities for cancer research and practices. Finally, we conclude that the interdisciplinary nature of cancer informatics and collaborations are major drivers for future research and applications in clinical practices. It is hoped that this review is instrumental for cancer researchers and clinicians with its informatics‐specific insights.