Applications of Artificial Intelligence in Drug Development and Diagnosis for Rare Diseases Current Status, Advantages, and Future Prospects

Abstract

Rare diseases, characterized by low prevalence and complex pathophysiological mechanisms, pose significant challenges in diagnosis and therapeutic development. Globally, an estimated 0.35 billion individuals are affected by rare diseases, with approximately 80% having a genetic basis. Conventional drug development approaches are often inadequate due to their high costs, long timelines, and low success rates. In recent years, rapid advancements in artificial intelligence (AI) have introduced transformative opportunities in the study and treatment of rare diseases. By integrating heterogeneous data sources—including genomics, proteomics, medical imaging, and electronic health records—AI has demonstrated substantial potential across multiple domains, such as biomarker discovery, target identification, virtual screening, drug repurposing, RNA-based therapeutics design, and clinical trial optimization. Notably, AI accelerates early-stage drug development, significantly reducing the time required for target validation and candidate selection while improving success rates. In diagnostics, AI leverages deep learning and natural language processing to enhance the accuracy and efficiency of genomic variant interpretation and phenotypic pattern recognition. Furthermore, AI contributes to clinical trial design by facilitating patient recruitment, optimizing trial protocols, and enabling the use of digital twin models to reduce the required sample size—an especially valuable capability given the limited patient populations in rare disease research. This review systematically examines key AI-driven strategies and their applications throughout the drug development pipeline for rare diseases. It also highlights how AI addresses critical challenges such as data scarcity, heterogeneity, and the need for personalized therapeutic interventions. Several representative case studies (e.g., AIT-101, PXT3003, REC-4881) illustrate the practical impact and future potential of AI in pharmaceutical innovation. As AI technologies continue to evolve and receive growing regulatory and policy support, they are poised to become a central force in advancing the diagnosis and treatment of rare diseases.