Rare diseases are characterized by low incidence rates, high heterogeneity, and significant genetic relevance, posing global challenges in clinical diagnosis and treatment, including delayed diagnosis and a scarcity of therapeutic options. Artificial intelligence (AI) technology offers novel solutions to address these challenges in the field of rare diseases. This paper explores the advancements in AI applications for rare diseases from two perspectives: auxiliary diagnosis and treatment decision-making. In terms of auxiliary diagnosis, AI can integrate superficial features, electronic health records, genomic data, and multi-modal data to achieve early and precise diagnosis. Regarding treatment decision-making, AI facilitates drug target discovery, drug repurposing, and the design of gene therapy vectors, thereby promoting the development and application of new treatments. Furthermore, this paper analyzes the challenges of AI in rare disease diagnosis and treatment concerning data, technical algorithms, and clinical application, and proposes future directions, including the construction of a collaborative data ecosystem, enhancement of algorithm interpretability, and improvement of regulatory frameworks.
