- Tech Insights
Shelly DeMotte Kramer,
Principal Analyst,Founding Partner, Futurum
AI and Healthcare: The Investment Landscape
A recent report from Silicon Valley Bank put numbers to what a glance around the industry would have clearly revealed: Investments in healthcare companies leveraging AI and machine learning are hot, hot, hot. Between 2015 and the first six months of 2017, venture deals raised a whopping $2.2 billion. The analysis focused on three kinds of AI technology to determine which was the top attraction for investors— Diagnostic Tests (DX tests) that yield yes or no results, Research and Development tools, and DX or Tools that use data analytics to help guide treatment. As you can see, from the results in Figure 1, the latter is the clear winner, yet all categories show impressive growth.
Examples of AI in Healthcare, Present and Future
It’s clear the funding is there, so what are companies doing with it? Let’s look at a few specific examples of how AI is going to revolutionize healthcare, both now and what it might look like in the future:
• Detecting hypertension and sleep apnea via a wearable. Healthtech company Cardiogram—a business that, according to the founder, applies deep learning to medicine—has developed DeepHeart,a neural network
technology that can be utilized on wearables like Apple Watches, Garmins, Androids, and even Fitbits. The goal of DeepHeart is to detect conditions like hypertension and sleep apnea—and, according to a study of the technology, it works. The study found it was 82 percent accurate when detecting hypertension. In addition, it was an impressive 90 percent accurate when detecting sleep apnea—a risk sometimes increased for women during menopause. A key point here is that both hypertension and sleep apnea are conditions that can often go undetected for some time, so DeepHeart provides peace of mind, right on your wrist. The findings of the report are so promising they were recently presented to the American Heart Association.
• Providing support in between visits to the doctor. Startup sensely has created a virtual nurse, Molly, who provides care for patients between doctor appointments. Molly services patients with chronic conditions, so cutting time in the office can save serious dollars and resources. Thanks to her vast neural networks, Molly gives customized advice, too—no canned, cold responses here.
• Making the complex world of medical records more efficient. The healthcare industry is chock full of data point after data point, record after record, including payment information, highly detailed medical records, and personal patient information including addresses, dates of birth, social security numbers, and more. This data conglomeration is called ePHI, and, although records are now required to be digital, that doesn’t mean they’re always easy to mine. (Sidebar: Read: Telemedicine and the Future of Healthcare for more on how important it is to protect ePHI.) The Google DeepMind Health project aims to fix that and then some, examining data from entire medical records—including test results—to improve patient care and help providers boost speed and efficiency. Imagine this scenario: What if AI could scan your test results, compare it with your medical history found in your record, and not only determine your risk for a particular condition, but even recommend a course of action—all, obviously, in seconds, not days or weeks? The technology to revolutionize healthcare in this way is on the horizon and is currently being tested at Moorfields Eye Hospital in London.
• Improving precision medicine. AI has the potential to change not just how patients are treated, but also the intricacies of common conditions at a molecular and genomic level, allowing researchers to better understand how to treat and identify common ailments. Deep Genomics, a Toronto startup, for example, aims to use AI to create genetic therapies. Their powerful mission statement reads, in part, “Our founding belief is that the future of medicine will rely on artificial intelligence, because biology is too complex for humans to understand.” A lofty statement, but one that’s looking more valid development-by-development. AI is also being used in precision medicine by companies like Human Longevity, a costly service that combines full body scans and complete genome sequencing to predict or identify very early a patient’s risk of vascular disease or cancer.
There’s more where that came from. As always, my colleagues and I will keep an eye on future AI advancements that will revolutionize healthcare—and there will be many. Stay tuned.