The Role of AI in Bridging Genomic Research and Clinical Diagnostic Models: A Comprehensive Review

Abstract

Abstract. Genomic research and clinical diagnostics will quickly be
reinvented around Artificial Intelligence (AI) to perform scalable and detailed
analysis of complex biological data and enhance the interpretability
of the clinically relevant variation in genomics. The increasing accessibility
of next-generation sequencing, whole-exome sequencing, electronic
health records, medical imaging and other multi-omics modalities
has opened a space in which computational systems capable of linking
molecular knowledge with real-world diagnostic processes can be created.
This review will discuss the utilisation of AI techniques (such as machine
learning, deep learning, generative AI, and multi-modal intelligence) to
assist variant calling, functional annotation, phenotype -genotype mapping,
disease risk prediction, biomarker discovery, and precision medicine.
The article integrates evidence on the topic of genomic diagnostics, AIbased
sequencing interpretation, and clinical decision support, considering
translational readiness, implementation limitation, and ethical issues.
The genomic discovery through the diagnostic deployment bridges by AI
are elaborated through a conceptual architecture and a tabular literature
review to explain the key avenues of interaction. The review concludes
that AI is not an ancillary analytic tool, and is core-central a translational
process of transforming genomic knowledge into clinical action and
patient-centred clinical models.