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The future of artificial intelligence (AI) in diagnosing rare genetic disorders is poised to transform precision medicine by accelerating the identification of conditions that are often difficult to diagnose. Rare genetic disorders, which affect millions of people worldwide, typically involve complex symptoms and lengthy diagnostic processes. AI's ability to process vast amounts of genomic, phenotypic, and clinical data positions it as a game-changer in this field. By detecting subtle patterns and correlations in large datasets, machine learning algorithms can deliver diagnoses faster and more accurately than traditional methods. AI-powered tools are proving valuable in whole genome and exome sequencing, automating the identification of pathogenic variants linked to rare diseases. By integrating clinical and phenotypic data, these systems can offer personalized insights, reduce diagnostic delays and improve genetic counseling and treatment development. However, the use of AI in rare disease diagnosis poses challenges, such as the need for diverse datasets to train algorithms and concerns over data privacy and equal access. Ensuring that AI tools are validated in diverse populations and effectively integrated into healthcare systems is crucial to their success. This summary will focus on the potential of AI to improve diagnostic accuracy, personalize treatments, and improve the management of rare diseases.
Buse Liv (Sat,) studied this question.