Rare diseases are characterized by very low incidence and prevalence rates, complex genetic mechanisms, and diverse clinical phenotypes, posing significant diagnostic and therapeutic challenges in clinical research. In principle, the design of clinical research protocols for rare diseases does not differ significantly from general clinical research. However, the difficulties mainly stem from the unique characteristics of rare diseases, which amplify the challenges and limitations inherent in general clinical research. These challenges typically involve five aspects: data collection, data management, technical methods, ethical regulations, and patient engagement. However, with the rapid development of digital technologies such as information technology, artificial intelligence (AI), and blockchain, particularly in the innovative applications of data collection, storage, analysis, sharing, and management, new opportunities have emerged for the implementation and optimization of rare disease clinical research. Strategies for conducting rare disease clinical research using digital technologies are often applied to rare disease clinical research and patient management based on digitalized registration platforms, the development of AI-driven diagnostic aids to improve the accuracy of rare disease diagnosis, the use of digital technologies for decentralized rare disease clinical research, and the promotion of data fusion from multiple sources and modalities. However, during the application process, new challenges have gradually been identified. Despite of many challenges that still exist in terms of data privacy, algorithmic fairness, and ethical norms, with the continuous maturation of technology and the improvement of ethical frameworks, digitally-intelligent-driven clinical research on rare diseases remains promising.

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