The future is now? Clinical and translational aspects of Omics technologies

Big data has become a central part of medical research, as well as modern life generally. Omics technologies include genomics, proteomics, microbiomics and increasingly other omics. These have been driven by rapid advances in laboratory techniques and equipment. Crucially, improved information handling capabilities have allowed concepts such as artificial intelligence and machine learning to enter the research world. The COVID-19 pandemic has shown how quickly information can be generated and analyzed using such approaches, but also showed its limitations. This review will look at how omics has begun to be translated into clinical practice. While there appears almost limitless potential in using big data for precision or personalized medicine, the reality is that this remains largely aspirational. Oncology is the only field of medicine that is widely adopting such technologies, and even in this field uptake is irregular. There are practical and ethical reasons for this lack of translation of increasingly affordable techniques into the clinic. Undoubtedly, there will be increasing use of large data sets from traditional (e.g. tumor samples, patient genomics) and nontraditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the health-care sector over the coming decade to integrate these resources in an effective, practical and ethical way.

Automated summary

Big data has become central in medical research and life, with Omics technologies being applied in the field. Despite the potential for precision medicine using big data, there are challenges in implementation.