AI in Genetic Variant Interpretation

A genetic variant is a difference in DNA sequence. Its clinical meaning is not obvious from the sequence alone: interpretation may require knowledge of the gene, disease mechanism, inheritance, the person’s phenotype, family segregation, population frequency, functional studies, and clinical observations. ACMG and AMP guidance provides a framework for classifying sequence variants into five categories: pathogenic, likely pathogenic, uncertain significance, likely benign, and benign. The categories are based on combinations of evidence criteria rather than a single model score. AI and computational tools can support curation by searching literature, extracting candidate evidence, or prioritizing variants for human review. A model may miss a relevant paper, misread an assay, overstate a computational prediction, or fail to apply a disease-specific criterion. ClinGen Variant Curation Expert Panels publish specifications that adapt guidance for particular genes or disorders. A result from one gene or population should not automatically be generalized to another. Clinical laboratories and qualified genetics professionals remain responsible for evidence evaluation, classification, and communication. A variant of uncertain significance is not a confirmed cause of disease and should not be used as if it were a pathogenic finding. Keep provenance for each evidence claim, check classifications against current criteria and databases, and state limitations clearly. AI can accelerate evidence organization, but it cannot replace validated curation, expert judgment, or patient-specific counseling. Record the reference genome build and transcript used.