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?:abstract
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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 analysed 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 \'personalised\' 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 datasets from traditional (e.g. tumour samples, patient genomics) and non-traditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the healthcare sector over the coming decade to integrate these resources in an effective, practical and ethical way.
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