AI in Variant Analysis: Fast Track to Genetic Diagnoses

Abstract

While falling costs have expanded access to genomic sequencing, clinical utility is frequently hindered by the challenge of interpreting complex genetic data. Although advances in genetic variant classification have improved diagnostic precision, they have also increased the identification of variants of uncertain significance (VUSs), widening the interpretation gap between data generation and clinical actionability. The high prevalence of VUSs can lead to false reassurance or psychological distress, as patients and non-expert clinicians may misinterpret inconclusive results. We propose that artificial intelligence (AI) is a critical clinical decision-support tool for bridging this gap, offering a scalable framework to optimize variant interpretation and shorten the diagnostic odyssey. We advocate integrating AI throughout the genetic diagnostic workflow–from initial phenotyping to variant prioritization–to facilitate data-driven, personalized treatment. We outline current AI-assisted approaches and discuss anticipated challenges in this pursuit, such as privacy, training data bias and quality, model explainability, and the necessity of a total product life cycle for validation. To address these challenges, we provide recommendations to ensure AI tools meet the highest standards of precision, reproducibility, and transparency. By standardizing AI across the variant analysis pipeline, we can fast-track the path to genetic diagnoses, effectively bridging the interpretation gap and enabling rapid delivery of personalized medical interventions.

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