February 2019

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No matter who is doing the counting, 2018 was an off-the-charts year for venture investment in digital health. Here’s a sampling of the tallies:

  • Our good friends at Rock Health put the total investment at $8.1 billion – an impressive 42 percent increase over 2017’s total of $5.7 billion.
  • The PwC/CB Insights MoneyTree Report posted an even higher total of $8.6 billion up from $7.1 posted in 2017.
  • StartUp Health reported $14.6 billion in global investment in digital health, up nearly 150 percent from 2017’s total of $6 billion (cited by MobiHealthNews).

And it’s not only that more money is going into the sector, it’s going into larger deals. Rock Health reports that the average deal size has grown to $21.9 million. That’s up from $15.9 million in 2017 and $13.6 million in 2016.


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The impact of artificial intelligence, or more specifically machine learning, is being felt in every industry sector, but perhaps nowhere more so than in healthcare, where AI funding hit historic highs in 2018, according to CB Insights.

The term AI is commonly used by the media and others to describe a computer-generated solution that is as good, or better than, a solution that could have been produced by a human. That often includes digital health tools that use algorithms programmed by researchers and clinicians. Machine learning is a subset of AI that uses neural networks to simulate or even expand on the level of data analysis that human minds are able to achieve. Deep learning is where software learns to recognize patterns. And these tools are already transforming diagnostic imaging.

The Scope

CB Insights reports that since 2013, $4.3 billion in private equity has been invested in healthcare AI startups across 576 deals. That’s more than AI startups in any other industry have taken in.

In a way, healthcare and AI are almost made for each other. The healthcare sector produces tons of data, but most of it is not being leveraged to provide the kind of insights it potentially could. The hope is that AI will be able to sort through this mountain of information to provide novel insights to improve the treatment or enable the prevention of disease.

In this post, we have rounded up some of the most promising applications for AI/ML in healthcare and examples of companies that are making it happen.


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