AI in variant analysis: fast track to genetic diagnoses - Elizabeth J. Wilk; Sasha Taluri; Timothy C. Howton; Anthony B. Crumley; Michal Mrug; Brittany N. Lasseigne (Human Genetics, 2026)
While falling costs have expanded access to genomic sequencing, clinical utility is frequently hindered by the challenge of interpreting complex genetic data. Variant analysis for rare disease patients especially requires significant time and expertise, creating a bottleneck that delays diagnostics. 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 by misinterpretting 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. While reclassification ultimately requires biological evidence that AI cannot replace, these tools serve as essential aggregators and prioritizers, especially as guidelines transition toward the upcoming quantitative ACMG v4 framework. 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 for "human-in-the-loop" design and intuitive workflow integration to ensure AI tools meet the highest standards of precision, reproducibility, and transparency to maximize adoption. 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.