Personalized Health Care Decisions Powered By Big Data And Generative Artificial Intelligence In Genomic Diagnostics

Abstract

Genomic diagnostics provide an essential tool for clinical decision-making since diseases can occur due to alterations at specific locations in the genome, especially when uncommon in prevalence. Genomic data are inherently complex and large, increasing the general need for sophisticated decision-support systems. Advancements in the further digitization of data and genomes, combined with efforts for closing the data collection gap, are generating enormous multidimensional datasets in this area. In general, potentially if volunteered by patients, the majority of the data is health-related. Novel and neglected but rapidly evolving technologies, including generative artificial intelligence, are currently enabling unprecedented opportunities in terms of automating complex and lengthy explorative data analyses. Actionable, health-related insights, which can be generated and interpreted by patients with increasing confidence from cherished or trusted digital hobbies outside the medical field, have the potential to more realistically change health behaviors. The ever-increasing data availability, as well as the increasing amounts of metabolomics, proteomics, epigenomics, and other ‘omics’ disciplines, biotechnology, and artificial intelligence innovation, especially in the fields of computational biology and bioinformatics, will pave the way toward a truly personalized medicine in genomic diagnostics. Integrating large data via comprehensive, personable systems into personalized health decisions could fundamentally change health behaviors, enabling precision health on all levels of health care: prevention, detection, treatment and follow-up. Anticipating that truly patient-centered genomic diagnostics will be available in the near future, individual people will have to address how aware they wish to become about body and health.