AI-driven enhancements in rare disease diagnosis and support system optimization

Abstract

Rare diseases are characterized by an extremely low prevalence, high phenotypic heterogeneity, and complex pathogenesis. This combination of factors presents significant challenges, including prolonged diagnostic delays, lack of standardized care, and difficulties in pathological interpretation. The integration of artificial intelligence (AI) offers a transformative approach to overcoming these barriers. In recent years, researchers worldwide have been actively exploring the use of AI to diagnose and manage rare diseases. Key advances include few-shot learning algorithms designed to tackle data scarcity, clinically validated foundation models that enhance diagnostic consistency across institutions, and multimodal AI frameworks that integrate imaging, genomic, and phenotypic data to improve diagnostic accuracy. In addition, there is growing recognition that AI can enhance diagnostic efficiency and thereby optimize support systems for rare diseases. As challenges such as AI model interpretability and data equity are addressed, AI is expected to make significant strides in the diagnosis and treatment of rare diseases.

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