Why we are excited to have invested in Aidence — moving the needle on healthcare

These days, it is impossible to read a tech blog without being bombarded by buzzwords around AI and Machine Learning. As a recovering AI entrepreneur (I founded an AI company back in ’99), this is obviously very exciting.

Just as in other areas of tech, innovation within this space is following the hype cycle, and in the last two decades, I have seen waves of excitement, followed by “nuclear winters”. But it’s not a controversial statement to say that fundamental changes are now happening, which will dwarf the impact of the smartphone era that started with the launch of the iPhone in 2007.

It may not yet be ubiquitous, but the vast possibilities are only beginning to be explored. There is rightfully a lot of excitement around tech companies that are grappling with the potential at the cutting-edge, like DeepMind and DigialPony and what they will be able to do in the future. But there are vast areas already where mainstream AI/ML has a fundamentally disruptive impact on the business models of today; with applications of natural language processing, deep learning and image recognition. Our latest investment, Aidence is a very good example of that.

The healthcare sector is struggling in many countries due to stretched resources, ageing populations, and issues around access. Proliferation of tech also tends to increase consumption of medical services, and this causes huge problems for the current medical system.

Aidence takes similar technology to that which is used by Facebook to tell you who is in your photos, and applies it to a fundamental problem of supporting medical diagnosis. Through deep learning it helps radiologists spot anomalies more accurately, even at the end of a long day. This is an application of AI that, rather than raising the specter of job destruction, helps controlling cost and improving quality in the healthcare space. And at the end of the day, patients will benefit.

Tech can often be smoke and mirrors (like lots of the ML pitches we see day-in day-out) or innovation for innovation’s sake. But what had us impressed in the case of Aidence, was the team’s deep understanding of how to really make change happen in a field as complicated and difficult to change as the healthcare industry. Covering areas from safeguarding privacy, working with regulation, and understanding the byzantine payer system, as well as state-of-the art AI, Aidence’s small team has achieved a lot already. We hope that by throwing our weight behind them we can help them accelerate towards their noble goals.