Investing in AI Healthcare Companies in 2020
Throughout the years we’ve been watching AI percolate throughout a diverse range of industries, from fintech and AI-powered investment banking to smart farming technologies. As we noted in our earlier articles on trends in artificial intelligence, healthcare is where machine learning and deep learning will pave the way to a new era of better drugs and smarter healthcare management. Machines are becoming as good as humans in their ability to interpret medical images. About 40% of healthcare providers reportedly use some form of AI-powered, computer-assisted diagnostics like chatbots or apps that offer personalized health advice based on a patient’s data, biometric inputs from wearables, or the rich datasets contained in today’s electronic healthcare records (EHRs).
While the investment opportunity into artificial intelligence has largely belonged to venture capital investors up to this point, the rapid growth of AI powered healthcare solutions means retail investors can now build an investment strategy around this emerging technology. Forget IBM and Dr. Watson – we’re talking about real pure play investments into AI healthcare companies here. We have compiled this Nanalyze Premium report for our readers where we identify seven stocks that are pure plays in this emerging space.
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Over the course of the past five years, Nanalyze has unearthed opportunities for retail investors to receive exposure to the exciting field of artificial intelligence (AI). With more than 3,000 “AI startups” around the globe focused on using machine learning, deep learning and other AI technologies to solve problems, investment opportunities have largely belonged to venture capital firms and corporate venture arms. The exceptions have been a handful of computer vision stocks, a pick-and-shovel investment in AI chips, and providers of AI powered solutions for healthcare organizations. In this report, we’re going to take a look at seven pure-play stocks that provide retail investors exposure to artificial intelligence in the healthcare sector.
The seven companies we cover in this report are active in diverse areas of AI healthcare, like natural language processing (NLP), medical imaging, drug discovery, and big data analytics. Each company has its own specific business segments, applications, and strategy that are relevant to the market niche they serve. There are some commonalities. For example, six out of seven are very recent IPOs. Five out of seven are in the early stages of dynamic growth – they are acquiring market share rapidly and consequently posting heavy losses. Most of these companies offer a scalable product to a well-defined target market.
In 2018, the global healthcare market reached a value of $8.5 trillion, with $3.65 trillion spent in the United States alone. There are very few trillion-dollar industries out there. If you’re able to capture even the tiniest percentage of that number, you can easily have a billion-dollar business.
There are also the feel-good aspects around investments that help democratize health care, improve the quality of life, and help save lives. Healthier people live longer, so more healthcare resources will be required to take care of tomorrow’s elderly. In the long run, investors can do well by doing good.
AI in the Healthcare Sector
Healthcare is one of the top industries impacted by artificial intelligence for many reasons. The global healthcare sector is not only massive but it’s also complex, full of inefficiencies, and has loads of patient data readily available in shoddy form. Imagine what AI systems can achieve in a sector where old-school data warehousing still hasn’t been mastered. Add to that the hefty price tag associated with anything medical (in the US the average hospital stay costs $10,700 and the average doctor earns $294,000 a year) and even tiny incremental efficiencies will make a huge difference to the bottom line. Among the many potential benefits, AI applications promise to improve patient outcomes, shorten drug development and clinical trials, and empower preventive and precision medicine. Besides the feel-good aspects of democratizing health care, the resulting efficiency gains are translated into hard dollars for investors in AI healthcare companies.
One potential investment thesis could focus on large corporations like IBM. For example, IBM’s Watson has seen considerable resources allocated to it, though nothing seems to be happening there at the moment. The Watson Healthdivision recently appointed a new leader who is doing the typical come to Jesus talk about getting back to basics, doubling down, and being laser focused on execution. It remains to be seen what – if anything – will come from this. (With that said, we do hold IBM as one of two technology stocks in our dividend growth investing (DGI) portfolio) Maybe IBM’s new leadership will be more transparent with shareholders about its AI healthcare strategy – if there is one – rather than a scattershot approach that seems based on generating publicity.
Another possible investment thesis for AI healthcare might surround medical device companies that are developing hardware and software solutions that capture tremendous amounts of healthcare data, which machine learning algorithmscan analyze to help decision making or provide predictive analytics like in the case of precision medicine. Medical device giants are actively dabbling here: Medtronic with its move into surgical robots and Stryker with its adoption of robotics and 3D printing. We hold both as part of our DGI strategy, but neither would be considered pure plays. (Read the Quantigence report to learn about the 30 DGI stocks we’re holding.)
The rapid growth of artificial intelligence in healthcare means retail investors can now build an investment strategy around this emerging technology. We have identified seven stocks operating in four distinct areas that we consider to be pure plays in the AI healthcare space. These areas are natural language processing, medical imaging, drug discover, and big data analytics.
Natural Language Processing
Much of our interaction with intelligent computer systems today involves communicating through text or voice command. Think about all of the clever (or not) chatbots that immediately start pestering you the moment you land on a new webpage. Or all the times smart voice assistants like Siri or Alexa respond (or not) to some trivial piece of trivia. These are all examples of a discipline of artificial intelligence known as natural language processing (NLP), which refers to the ability of the machine to read language and turn it into structured data. Under NLP there are two additional categories:
- Natural Language Understanding (NLU): the understanding of human language by computer.
- Natural Language Generation (NLG): the ability of machines to write things like reports and sports stories.
While NLP is the umbrella term used here, much of the technology behind the ability of virtual assistants, for example, to answer healthcare questions actually relies on NLU, which has come on the scene much more recently. Ditto for the sort of voice recognition technology employed by our first company.
One of the areas of AI healthcare where we see tremendous interest and progress is the use of machine learning or deep learning algorithms to interpret medical images – X-rays, MRIs, and other types of scans – and offer a diagnosis or even prognosis. While we would expect a behemoth like IBM to dominate in this emerging market, it’s really the startups that are the future of medical imaging, particularly in oncology where AI is becoming at least as good doctors in spotting tumors.
Developing new drugs isn’t for the faint of heart. One oft-quoted study found that pharmaceutical companies need more than a decade and upwards of $1.3 billion to bring a drug to market. AI algorithms can not only help jumpstart the process by rapidly identifying promising molecules out of billions of iterations, but companies are also developing AI solutions to expedite clinical trials, which are the most expensive phase of the process. And a few of the more than 200 startups working on various aspects of AI driven drug discovery do it all. The use of machine learning in drug discovery and development is really the latest tool in the digitization of this sector of the biotech and pharmaceutical industries, building on a strategy that’s become known as computational drug discovery, where advances in computing, sequencing, modeling, and more have already helped accelerate the process. Machine learning takes even more of the guesswork out of the equation, predicting everything from potential toxicities to how a drug candidate might interact with different body tissues and chemistry.
Big Data Analytics
It’s no secret that an AI algorithm is only as good as the data you feed it. In the last few years, plenty of companies have come along that apply artificial intelligence to the trove of digitized data that has been created. These data sciencecompanies are able to provide insights into everything from financing and trading to selling more soda. In industries that haven’t seen a lot of operational innovation over the years – like healthcare – patient data analytics companies are catching up quickly. Big data paired with AI can help shrink down the size of medical devices or power apps that help manage our healthcare. Some offer enterprise systems with learning algorithms that ingest thousands, if not millions, of data points to improve patient outcomes while cutting costs, such as in hospital operations.
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7 pure-play AI Healthcare stocks
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