To summarize the applications of data-intelligence technology in diagnosing lysosomal storage disease(LSD), analyze their opportunities and challenges in clinical practice as well as their development trends, and provide insights and recommendations for advancing digitally driven auxiliary diagnostic technologies.

A comprehensive literature search was conducted across databases including PubMed, Web of Science, Embase, CNKI, Wanfang Database, and VIP. The studies focusing on the application of digital-intelligence technologies in LSD diagnosis were included. A qualitative analysis was performed, categorizing and summarizing research based on the types of digital-intelligence technologies employed, and exploring future development trends.

The analysis revealed that digital-intelligence technologies, particularly in areas such as big data storage and management, data mining and analytics, machine learning, natural language processing, and computer vision, held significant potential for early screening and diagnosis of LSD. These technologies facilitated the identification of potential patients, discovery of new biomarkers, quantitative analysis of symptoms, and elucidation of gene-disease relationships, ultimately enhancing diagnostic efficiency and accuracy.

Digital-intelli-gence technologies present promising prospects for advancing LSD diagnostic research and improving diagnostic precision. Future efforts should focus on developing a comprehensive, multidimensional diagnosis system and diagnostic technologies under the guidance of the DI-HEALTH theoretical framework, in the hope of paving the way for further development of digitally assisted diagnostic solutions.