Authors: Liyana Cherian
Abstract: Artificial intelligence (AI) has emerged as a transformative force in the field of rare disease diagnosis, addressing long-standing challenges associated with these complex and often overlooked conditions. Rare diseases, characterized by their low prevalence and high clinical heterogeneity, have traditionally posed significant diagnostic difficulties due to the scarcity of specialized knowledge and limited patient data. AI technologies—including machine learning, deep learning, and natural language processing—have demonstrated the potential to enhance diagnostic accuracy, reduce delays, and personalize patient care. These technologies leverage vast amounts of medical data, including genomic information, clinical records, and imaging studies, to identify patterns and generate insights that human clinicians may overlook. This article explores the multifaceted role of AI in rare disease diagnosis, examining its applications, benefits, challenges, and ethical considerations. Emphasis is placed on how AI can integrate diverse data sources to support differential diagnosis and how it facilitates research into rare disease mechanisms and therapeutics. As AI continues to evolve, its integration into clinical workflows will require collaboration across disciplines, robust validation, and attention to data privacy to ensure equitable access and trustworthiness. This article aims to provide a comprehensive review of the current landscape and future prospects of AI in rare disease diagnosis, highlighting its transformative potential and the critical steps needed to translate AI advancements into improved patient outcomes.
DOI: http://doi.org/10.5281/zenodo.16980317
