Artificial Intelligence: A New Frontier in Rare Disease Early Diagnosis

Rare diseases present significant challenges, including delays in diagnosis, inadequate treatment responses, and difficulties in monitoring. These challenges arise from the complexity of symptoms, limited medical expertise, and insufficient diagnostic tools. Artificial Intelligence (AI) has gained attention for its potential to improve healthcare, particularly in diagnosing complex conditions. By analyzing large datasets, recognizing patterns, and integrating clinical information, AI can refine diagnostic accuracy, enhance treatment strategies, and improve patient outcomes. This literature review examines AI applications in three key areas of rare disease diagnosis: genetic analysis, imaging-based phenotyping, and natural language processing (NLP) for clinical data extraction. AI-driven advancements in these domains have been explored to improve disease detection and management. However, several challenges persist, including limited data availability, algorithm transparency, privacy considerations, and ethical concerns. Efforts such as data augmentation and transfer learning are being explored to address these issues and expand AI's role in clinical practice. By refining diagnostic processes and optimizing treatment strategies, AI has the potential to improve the management of rare diseases. This review critically examines AI's role in rare disease diagnosis, with a particular emphasis on its applications in genetic analysis, imaging-based phenotyping, and NLP, while also addressing key challenges and future directions for clinical integration.