Switzerland based SaaS startup Sophia Genetics is hoping to give IBM Watson a run for its money in the healthcare diagnostics space. It's built a big data analytics platform that harnesses clinicians' medical expertise to enhance genomic diagnostic via AI algorithms -- leading, it says, to better and faster diagnoses for patients with diseases such as cancer. Hospitals that use the platform are intended to jointly benefit from expert-fed, algorithmic DNA sequencing diagnostic insights exactly because they are shared across the platform. So as the user-base scales -- it says it's adding 10 new hospitals each month -- Sophia Genetics' AIs get smarter and more accurate, and patients anywhere can benefit from the pooled knowledge. The company is announcing a $30 million Series D funding round today, adding UK-based VC firm Balderton Capital to its investor roster, along with 360 Capital Partners.

Jurgi Camblong is a self-confessed dreamer. He believes it's possible to "democratise data-driven medicine" by standardising data analytics around the world. Founded in 2011, Camblong's Swiss-based Sophia Genetics is trying to do this with artificial intelligence and machine learning. Camblong, speaking at the WIRED Health conference in London, announced that in the next few weeks the firm will have analysed the genomic profiles of 100,000 people. Genome sequencing is the practice of decoding a person's DNA, a process that creates colossal amounts of data.

Jurgi Camblong plans to work with "liquid biopsies" making the process less invasive and faster This article was first published in the June 2016 issue of WIRED magazine. Be the first to read WIRED's articles in print before they're posted online, and get your hands on loads of additional content by subscribing online. Jurgi Camblong is diagnosing cancer using thousands of people's DNA. The 38-year-old Sophia Genetics co-founder detects cancer in the lungs, skin, ovaries and breast, as well as congenital diseases, by sequencing the genomes of patient's tissue samples – then uses machine learning to compare the results and suggest the most effective treatments. "The problem is not producing the content or the data but really analysing to find the important information so you can act on a disease," says Camblong.