Applications of artificial intelligence in clinical laboratory genomics

• Published in: American journal of medical genetics. Part C, Seminars in medical genetics Vol. 193; no. 3; p. e32057
• Main Authors: Aradhya, Swaroop, Facio, Flavia M, Metz, Hillery, Manders, Toby, Colavin, Alexandre, Kobayashi, Yuya, Nykamp, Keith, Johnson, Britt, Nussbaum, Robert L
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.09.2023
• Subjects: Artificial Intelligence; Big Data; Data analysis; Deoxyribonucleic acid; DNA; DNA sequencing; DNA structure; Ethical standards; Genetic diversity; Genetic screening; Genetic Testing; Genetic variance; Genetics; Genomic analysis; Genomics; Genomics - methods; Humans; Laboratories, Clinical; Medical laboratories; Natural language processing; Pathogenicity; Pathogens; Phenotype; Phenotypes; Protein structure; Structure-function relationships; deep learning; germline genetic testing; variant classification; precision medicine; in silico prediction algorithms; machine learning
• ISSN: 1552-4868; 1552-4876; 1552-4876
• DOI: 10.1002/ajmg.c.32057

The transition from analog to digital technologies in clinical laboratory genomics is ushering in an era of "big data" in ways that will exceed human capacity to rapidly and reproducibly analyze those data using conventional approaches. Accurately evaluating complex molecular data to facilitate timely diagnosis and management of genomic disorders will require supportive artificial intelligence methods. These are already being introduced into clinical laboratory genomics to identify variants in DNA sequencing data, predict the effects of DNA variants on protein structure and function to inform clinical interpretation of pathogenicity, link phenotype ontologies to genetic variants identified through exome or genome sequencing to help clinicians reach diagnostic answers faster, correlate genomic data with tumor staging and treatment approaches, utilize natural language processing to identify critical published medical literature during analysis of genomic data, and use interactive chatbots to identify individuals who qualify for genetic testing or to provide pre-test and post-test education. With careful and ethical development and validation of artificial intelligence for clinical laboratory genomics, these advances are expected to significantly enhance the abilities of geneticists to translate complex data into clearly synthesized information for clinicians to use in managing the care of their patients at scale.