… well as their multi-omics/genomic data to reach a conclusion … that is, artificial intelligence,
healthcare, clinical genomics, and … with artificial intelligence, healthcare, clinical genomics, and …

… Next, we focus on emerging methods for specific tasks in clinical genomics, including
variant calling, genome annotation and variant classification, and phenotype-to-genotype …

… models have important uses in clinical genomics, such as in text mining and simple chatbots…
Points in clinical laboratory genomics where artificial intelligence methods can be applied. …

… Progress in artificial intelligence and machine learning, along with the accessibility of cloud
… tool in clinical oncology. However, important challenges remain in cancer genomics and …

… and genomic medicine combined with artificial intelligence … Clinical, pathological, and
epidemiological data are analyzed … It analyzes clinical, molecular, and genomic data to validate …

… Active involvement of the clinical genomics and medical informatics communities is critical
for successful validation and deployment of AI in genomics. AI algorithms implicitly subscribe …

… clinically-actionable knowledge. To accomplish this, many researchers are turning toward
machine learning (ML), an approach of artificial intelligence … quantities of genomic sequence …

… interpretation of genomics through artificial intelligence and machine learning approaches
plays … are linked with clinical data, including the type and treatments implemented. Citation73 …

… Some of the challenges are technical in nature, including insufficient interoperability of
clinical genomic data. Other challenges go beyond the technical realm to include ethical and …

… In this section, we introduce the AI techniques used in clinical genomic applications from
a … are included because of their significant contribution to clinical genomic applications. …