# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 3 Artificial Intelligence in Genetics and Genomics  
### AI in Genetics and Computational Genomics  
The convergence of artificial intelligence (AI) with genetic sciences has ushered in a new era of innovation in genomics, driven by the need to analyze vast quantities of biological data [42]. AI algorithms, especially those in the realms of ML and deep learning (DL), are pivotal in addressing the complex and multifaceted challenges posed by modern genomics [43]. With the advancement of high-throughput sequencing technologies, such as next-generation sequencing (NGS) and single-cell sequencing, the quantity of genomic data generated has exponentially increased. Interpreting this massive volume of data necessitates sophisticated computational tools that are capable of handling both the scale and complexity of the human genome, which is where AI systems have demonstrated transformative potential. A key application of AI in genomics is in variant calling. Genomic variation, such as single nucleotide polymorphisms (SNPs), insertions, deletions, and structural variants, are critical to understanding human disease, evolution, and genetic diversity [44]. Traditional approaches to variant calling, which heavily rely on statistical models, often struggle with distinguishing true variants from sequencing errors, particularly in the repetitive or poorly characterized regions of the genome [45]. AI, and more specifically deep learning models such as convolutional neural networks (CNNs), have been successfully employed to overcome these limitations by learning complex patterns directly from raw sequencing data [46]. For example, Google DeepVariant (available at [[https://github.com/google/deepvariant](https://github.com/google/deepvariant)]([https://github.com/google/deepvariant](https://github.com/google/deepvariant)), accessed on 5 February 2025) is an AI-powered tool that uses deep learning techniques to improve the accuracy of variant calling, significantly reducing error rates compared to traditional bioinformatics pipelines. DeepVariant was trained on a diverse set of genomic data to predict genetic variants directly from sequencer reads, demonstrating how AI can surpass rule-based methods and enhance the fidelity of genomic data interpretation. Beyond variant calling, AI plays a crucial role in functional genomics, particularly in predicting the functional impact of genetic variants. Not all genetic variants are functionally significant; some may be benign, while others can have profound effects on gene regulation, protein structure, or cellular pathways [47]. AI models, such as those based on recurrent neural networks (RNNs) and attention mechanisms, have been used to predict the pathogenicity of variants [48]. These models are trained on annotated databases, like ClinVar ([[https://www.ncbi.nlm.nih.gov/clinvar/](https://www.ncbi.nlm.nih.gov/clinvar/)]([https://www.ncbi.nlm.nih.gov/clinvar/](https://www.ncbi.nlm.nih.gov/clinvar/)), accessed on 5 February 2025) and the Genome Aggregation Database (gnomAD) ([[https://gnomad.broadinstitute.org](https://gnomad.broadinstitute.org)]([https://gnomad.broadinstitute.org](https://gnomad.broadinstitute.org)), accessed on 5 February 2025), which contain information on the clinical significance of known variants. By leveraging AI, it is possible to prioritize variants of unknown significance (VUSs) for further study, thus accelerating the identification of potential disease-causing mutations [42]. Additionally, AI-driven approaches like AlphaFold, developed by DeepMind, have revolutionized protein structure prediction, which is inherently linked to genomics [49]. AlphaFold's ability to accurately predict 3D protein structures from amino acid sequences has opened new avenues in understanding how genetic variants affect protein folding and function, which is crucial for identifying novel drug targets and understanding the molecular basis of diseases.