Glorikian’s New Book Sheds Light on Artificial Intelligence Advances in the Healthcare Field

427
0

WATERTOWN — Like many of us, I usually tend to keep up with developments in technology as they reach us in dribs and drabs, without really paying attention to the overall picture. Yet according to some experts, we are in a period of transformation of all aspects of life and society as revolutionary as that of the original Industrial Revolution. Healthcare entrepreneur and global business expert Harry Glorikian, an Armenian-American based in the Boston area, focuses our attention on how artificial intelligence (AI) and big data during this transformation affect healthcare in his new book The Future You: How Artificial Intelligence Can Help You Get Healthier, Stress Less, and Live Longer (Dering Harbor, New York: Brick Tower Press, 2021).

The book several months ago was on the top ten list of Amazon.com best-sellers and comes highly recommended. For example, New York Times bestselling author of 14 books on nutrition and health, and a television medical correspondent, Bob Arnot, M.D., in his brief introduction in the book recommends it be turned into the centerpiece of your coffee table. He exhorts, “It can make all the difference in the quality of your life.”

It is 272 pages long, including 52 pages of references to articles, which are mostly accessible online, but in its core sections, it is a fast-moving and lively work intended for a broad audience. Artificial intelligence is the simulation of human intelligence processes by machines, or in other words the ability of a device to teach itself to learn and think. Glorikian examines its use along with “big data” and predictive analytics in healthcare and proclaims at the start of his book “There’s not a person among us who isn’t going to feel the effects of this AI revolution, and it’s going to utterly transform the healthcare system as we know it today.”

Glorikian’s Expertise

At the start of the book, Glorikian relates how he came into this field. His story began in 1984, when as a 19-year-old, he worked for a man in the computer education business who also invented a type of computer to help him predict winning bets on roulette and other games of chance. This was his first insight into the benefits of predictive analytics and throughout most of his career, he wrote, he would “peer down the pike and tell my employer, fellow executives, or investors which products or business model will be the next best thing in healthcare” (p. 13).

Harry Glorikian

Flashing forward to 1999, Glorikian ends up working for Applied Biosystems, where scientists worked on the mapping of the human genome, combining the use of biology and information technology. Meanwhile, when his sister-in-law and husband are visiting and he is touring them around the Boston area, he came up with the idea of using GPS data to provide information on touristic sites or restaurants in the vicinity of travelers but was not able to break into what then was the telecom industry.

Get the Mirror in your inbox:

In 2013, he began work on Moneyball Medicine, coauthored with award-winning medical science writer Malorye Allison Branca. This was his first book, and while it also is on how data-driven technology was affecting healthcare and the life sciences, it targeted people in these industries, not a general audience. It was published in 2017, and his second book, an even more specialized work about the business aspect of in vitro diagnostics (IVD – tests done on samples such as blood or tissue that have been taken from the human body) titled Commercializing Novel IVDs: A Comprehensive Manual for Success, was published the following year.

From 2014 to 2016, he served as an Entrepreneur-In-Residence to GE Ventures – New Business Creation Group, leading to the founding of Evidation Health and DrawBridge Health. Today, Glorikian is a general partner at Scientia Ventures, a venture capital firm focusing on promising tech companies using AI and big data to make diagnostic devices and new therapies, so he continues to scan new technology to see what may be significant for healthcare. He also serves on the boards of various companies.

In addition to periodically giving interviews to various media outlets, Glorikian has his own podcast series called the Harry Glorikian Show, where he speaks to leaders in the healthcare and life sciences industries. The distribution is worldwide, so that he has listeners in many different countries, and recently the series attained the top 2.5 percent in global ranking for such shows. Glorikian said that its approach “depends on the guest, but I try to tilt it towards everybody. Once in a while it does get a little wonky – I can’t help myself sometimes.” An average show, he said, will get several thousand listens.

Glorikian holds an MBA from Boston University and a bachelor’s degree in biology from San Francisco State University.

Transformations

Glorikian begins The Future You by explaining basic terms. Machine learning is a subset of AI, while deep learning is in turn a specific kind of machine learning. Neural networks attempt, he writes, to mimic how the human brain works through deep learning algorithms or mathematical “sentences.” Big data refers to extremely large sets of data about everything, not only traditionally structured and easily accessible data, that may reveal various trends or associations through computational analysis.

Illustration from The Future You on the nesting doll relationship between AI, machine learning, deep learning and neural networks (courtesy Harry Glorikian)

After describing various ways in which AI and big data are involved already in our daily lives, ranging from the food we eat, the cars we drive and the things we buy, he concludes that it is leading to the Fourth Industrial Revolution, a phrase coined by Klaus Schwab, the head of the World Economic Forum. All aspects of life will be transformed in a way analogous to the prior industrial revolutions (first the use of steam and waterpower, second the expansion of electricity and telegraph cables, and third, the digital revolution of the end of the 20th century).

At the heart of the book are the chapters in which he explains what data and AI have already accomplished for our health and what they can do in the future. The ever-expanding amount of personal data available combined with advances in AI allows for increasing accuracy of diagnoses, treatments and better sensors and software. Glorikian notes that today there are over 350,000 different healthcare apps and the mobile health market is expected to approach $290 billion in revenue by 2025.

Glorikian employs a light, informal style of writing, with references to pop culture such as Star Trek. He asks the reader questions and intersperses each chapter with what he calls sidebars. They are short illustrative stories or sets of examples. For example, “AI Saved My Life: The Watch That Called 911 for a Fallen Cyclist” (p. 68) starts with a man who lost consciousness after falling off his bike, and then lists other ways current phones can save lives. Other sidebars explain basic concepts like the meaning of genes and DNA; or about gene editing with CRISPR.

Present and Future Advances

Before getting into more complex issues, Glorikian describes what be most familiar to readers: the use of AI-enabled smartphone apps which guide individuals towards optimal diets and exercising as well as allow for group activities through remote communication and virtual reality. There are already countless AI-enabled smartphone apps and sensors allowing us to track our movements and exercise, as well as our diets, sleep and even stress levels. In the future, their approach will become more tailored to individual needs and data, including genomics, environment, lifestyle and molecular biology, with specific recommendations.

He speculates as to what innovations the near future may bring, remarking: “What isn’t clear is just how long it will take us to move from this point of collecting and finding patterns in the data, to one where we (and our healthcare providers are actively using those patterns to make accurate predications about our health.” He gives the example of having an app to track migraine headaches, which can find and analyze patterns in the data (do they occur on nights when you have eaten a particular kind of food or traveled on a plane, for example). Eventually, at a more advanced stage, it might suggest you take an earlier flight or eat in a different restaurant that does not use ingredients that might be migraine triggers for you.

Healthcare will become more decentralized, Glorikian predicts, with people no longer forced to wait hours in hospital emergency rooms. Instead, some issues can be determined through phone apps and remote specialists, and others can be handled at rapid care facilities or pharmacies. Hospitals themselves will become more efficient with command centers monitoring the usage of various resources and using AI to monitor various aspects of patient health. Telerobotics will allow access to specialized surgeons located in major urban centers even if there are none in the local hospital.

In the chapter on genetics, Glorikian presents three ways in which unlocking the secrets of an individual’s genome can have practical health consequences right now. The first is the prevention of bad drug reactions through pharmacogenomics, or learning how genes affect response to drugs. Second are enhanced screening and preventative treatment for hereditary cancer syndromes. One major advancement just starting to be used more, notes Glorikian, is liquid biopsy, in which a blood sample allows identification of tumor cells as opposed to standard physical biopsies. It is less invasive and sometimes more accurate for detecting cancers prior to the appearance of symptoms. The third way is DNA sequencing at birth to screen for many disorders which are treatable when caught early. The future may see corrections of various mutations through gene editing.

He points out the various benefits in the health field of collecting large sets of data. For example, it allows the use of AI or machine learning to better read mammogram results and to better predict which patients would see benefit from various procedures like cardiac resynchronization therapy or who had greater risk for cardiovascular disease. There is hope that this approach can help detect the start and the progression of diseases like Alzheimer’s or diabetic retinopathy. Ultimately it may even be able to predict fairly reliably when individuals would die.

At present, AI accessing sufficient data is helping identify new drugs, saving time and money by using statistical models to predict whether the new drugs will work even before trials. AI can determine which variables or dimensions to remove when making complex computations of models in order to speed up computational processes. This is important when there are large numbers of variables and vast amounts of data.

Glorikian does not miss the opportunity to use the current Covid-19 crisis as a teaching moment. In a chapter called “Solving the Pandemic Problem,” Glorikian discusses the role AI, machine learning and big data played in the fight against the coronavirus pandemic, in spotting it early on, predicting where it might travel next, sequencing its genome in days, and developing diagnostic tests, vaccines and treatments. Vaccine development, like drug development, is much faster today than even 20 years ago, thanks to computational modeling and virtual clinical trials and studies.

Potential Problems

Glorikian does not shy away from raising some of the potential problems associated with the wide use of AI in medicine, such as the threat to patient privacy and ethical questions about what machines should be allowed to do. Should genetic editing be allowed in humans for looks, intelligence or various types of talents? Should AI predictions of lifespan and dates of death be used? What types of decisions should machines be allowed to make in healthcare? And what sort of triage should be allowed in case of limited medical resources (if AI predicts one patient is for example ten times more likely to die than another despite medical intervention)? There are grave dangers if hackers access databanks or medical machines.

There are also potential operational problems with using data as a basis for AI, such as outdated information, biased data, missing data (and how it is handled), misanalyzed or differently analyzed data.

Despite all these issues, Glorikian is optimistic about the value of AI. He concludes, “But despite the risk, for the most part, the benefits outweigh the potential downsides…The data we willingly give up makes our lives better.”

Armenian Connection

When asked at the end of June, 2022 how Armenia compares with the US and other parts of the world in the use of AI in healthcare, he made the distinction between the Armenian healthcare system and Armenian technology that is directed at the world healthcare system.

On the one hand, he said, “I don’t know of a lot that is being incorporated into the healthcare system, although we do have a national electronic medical record system that they have really been improving on a consistent basis.” Having such a health record system throughout the country will provide data for the next step in use of AI, and that, he said “is very exciting.”

On the other hand, for technology companies involved in healthcare and biotechnology in Armenia, he said, “I would always like to see more, but there are some really interesting companies that have sprouted up over the last five years. Also, with the tech giant NVDIA opening up a research center in Armenia, Glorikian said he hoped there will be interesting synergies since this company does invest in the healthcare area.

Harry Glorikian, second from left, next to Acting Prime Minister Nikol Pashinyan, in a December 19, 2018 Yerevan meeting

At the end of 2018, Glorikian met with then Acting Prime Minister Nikol Pashinyan to discuss launching the Armenian Genome project to expand the scope of genetic studies in the field of healthcare. He said that this undertaking was halted for reasons beyond his understanding. He said, “My lesson learned was you can move a lot faster and have significant impact by focusing on the private sector.”

Indeed, this is what he does, as an individual investor, although he finds investing as a general partner of a fund more impactful. He is also a member of the Angel Investor Club of Armenia. While the group looks at a broad range of companies, mainly technology driven, he and a few other people in it take a look at those which are involved in healthcare. In fact, he is going to California at the very end of June to learn more about a robot companion for children called Moxie, prepared by Embodied, Inc., a company founded by veteran roboticist Paolo Pirjanian. Pirjanian, who was a guest on Glorikian’s podcast several weeks ago, lives in California, but Glorikian said that the back end of his company’s work is done in Armenia.

Glorikian added that he is always finding out about or running into Armenians in the diaspora doing work with AI.

Changes

When asked what has changed since the publication of the book last year, he replied, “Things are getting better!” While hardware does not change overnight, he said that there have been incremental improvements to software during the period of time it took to write the book and then have it published. He said, “For someone reading the book now, you are probably saying, I had no idea that this was even available. For someone like me, you already feel a little behind.”

Readers of the book have already begun to contact Glorikian with anecdotes about what it led them to find out and do. He hopes the book will continue to reach more people. He said, “The biggest thing I get out of it is when someone says I learned this and I did something about it.” When individuals have access to more quantifiable data, not only can they manage their own health better, but they also provide their doctors with more data longitudinally that helps the doctor to be more effective. Glorikian said this should have a corollary effect of deflating healthcare costs in the long run.

One minor criticism of the book, at least of the paperback version that fell into the hands of this reviewer, is the poor quality of some of the images used. The text which is part of those illustrations is very hard to read. Otherwise, this is a very accessible read for an audience of varying backgrounds seeking basic information on the ongoing transformations in healthcare through AI.

Get the Mirror-Spectator Weekly in your inbox: