AI for Rare Disease Diagnosis: Overcoming Challenges in Healthcare Inequity

Abstract

Diagnosing rare diseases is a significant challenge due to limited data and expertise. AI offers innovative solutions by analyzing large datasets to identify patterns and correlations that are often overlooked. This paper examines the role of AI in rare disease diagnosis, focusing on natural language processing for analyzing medical literature, and machine learning for phenotype-genotype correlations. The discussion includes case studies, the impact of AI on reducing diagnostic delays, and the importance of global data-sharing initiatives to enhance AI model performance.