From the stethoscope to the CT scanner, technology and healthcare have long gone hand in hand. But the difficulty, especially in an age when budgets are stretched and digital innovations are proliferating, is deciding which technologies will deliver the biggest public health benefits. These need not be cutting-edge innovations. One important step is simply to replace existing analogue systems with digital ones, says David Maguire, senior analyst in the policy team at health think-tank the King's Fund. This year, Maguire co-authored a report that analysed the evidence on digital technology in health and social care. Among the most promising areas it identified was communications, both internally and when dealing with patients.

Healx has raised $56 million (€51 million) to launch 40 rare disease programs while taking some of its existing assets into the clinic. The ambitious target is enabled by an AI drug discovery platform that Healx thinks gives it a scalability that has more in common with tech than biotech. David Brown, the former global head of drug discovery at Roche, and Tim Guilliams cofounded Healx in 2014 to use AI to find opportunities to repurpose existing molecules to treat rare diseases. After quietly working on the technology in its early years, Healx raised $10 million last year to build on its early success in identifying a potential treatment for Fragile X syndrome. That money was due to see Healx through to 2020 but Guilliams, who works as CEO, decided to pull the financing forward in light of the success rate of the company's predictions and translations.

Healx, a company using artificial intelligence (AI) to discover new drug treatments for rare diseases, has raised $56 million in a series B round of funding led by Atomico, with participation from Intel Capital, Balderton Capital, Global Brain, Btov Partners, Amadeus Capital Partners, and Cambridge Innovation Capital's Jonathan Milner. Founded out of Cambridge in 2014, Healx's core AI Platform -- Healnet -- applies a range of machine learning techniques to public and proprietary data sources, covering literature, clinical trials, patents, drug targets, chemical structures, symptoms, and more. Part of this process involves using natural language processing (NLP) to extract insights and knowledge from all the published sources around specific diseases. The culmination of all this data, Healx CEO and cofounder Dr. Tim Guilliams said, is a knowledge graph of rare diseases that could help pharmacologists or biologists unearth effective new treatments that would otherwise be much more difficult to spot. "We use a variety of machine learning algorithms to solve the many tasks necessary to predict drug treatments and translate them in the clinic effectively," Guilliams told VentureBeat.