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In genetics, ai refers to using sophisticated computational methods to analyze and interpret genetic data However, the vast majority of ai solutions. This multidisciplinary discipline uses ai to decipher genetic data and provide academics and medical professionals with a better understanding of the complicated functions of the genome.
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After introductory explanations and examples, the review discusses ai in clinical genetics in three main categories In genetics, a number of ai use cases have been published, from improving the performance of gene sequencing [2, 3] to developing early detection tools for genetic conditions [3, 4] Artificial intelligence and genetic testing artificial intelligence (ai) has become important in interpreting data from genetic tests, from sorting through large amounts of data to identifying sequencing errors
While ai’s role in genetic testing is expected to increase in the coming years, involvement of humans and having the “human touch” remains essential
With increasing complexity in genomic data, researchers are turning to artificial intelligence and machine learning as ways to identify meaningful patterns for healthcare and research purposes. Artificial intelligence (ai) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new ai platforms Background “artificial intelligence” (ai) based systems developed for healthcare continue to make headlines, with new studies released on a weekly basis [1]
