Applications of Artificial Intelligence in Genetic Data Analysis and Variant Interpretation

Artificial intelligence (AI) has emerged as a transformative tool in the field of genetics, particularly in the analysis of large-scale genomic data and the interpretation of genetic variants. With the rapid expansion of next-generation sequencing technologies, the volume and complexity of genetic information have exceeded the capacity of traditional analytical methods. AI-based approaches, including machine learning and deep learning algorithms, provide powerful solutions for identifying pathogenic variants, predicting genotype–phenotype associations, and improving diagnostic accuracy in hereditary and complex diseases. These methods can integrate diverse datasets, such as genomic, transcriptomic, and clinical information, thereby supporting more comprehensive and precise interpretations. In addition, AI contributes to the prioritization of variants of uncertain significance, which remains a major challenge in clinical genomics. Despite these advantages, several limitations must be considered, including data bias, lack of interpretability, model generalizability, and ethical concerns related to privacy and clinical implementation. This study reviews the current applications of AI in genetic data analysis and variant interpretation, highlighting its potential to advance precision medicine while also addressing the key technical and ethical challenges associated with its use in genomic research and clinical practice.

کلیدواژه ها:

Artificial Intelligence ، Genetic Data Analysis ، Variant Interpretation ، Precision Medicine ، Machine Learning