Tag Archives: health

#434792 Extending Human Longevity With ...

Lizards can regrow entire limbs. Flatworms, starfish, and sea cucumbers regrow entire bodies. Sharks constantly replace lost teeth, often growing over 20,000 teeth throughout their lifetimes. How can we translate these near-superpowers to humans?

The answer: through the cutting-edge innovations of regenerative medicine.

While big data and artificial intelligence transform how we practice medicine and invent new treatments, regenerative medicine is about replenishing, replacing, and rejuvenating our physical bodies.

In Part 5 of this blog series on Longevity and Vitality, I detail three of the regenerative technologies working together to fully augment our vital human organs.

Replenish: Stem cells, the regenerative engine of the body
Replace: Organ regeneration and bioprinting
Rejuvenate: Young blood and parabiosis

Let’s dive in.

Replenish: Stem Cells – The Regenerative Engine of the Body
Stem cells are undifferentiated cells that can transform into specialized cells such as heart, neurons, liver, lung, skin and so on, and can also divide to produce more stem cells.

In a child or young adult, these stem cells are in large supply, acting as a built-in repair system. They are often summoned to the site of damage or inflammation to repair and restore normal function.

But as we age, our supply of stem cells begins to diminish as much as 100- to 10,000-fold in different tissues and organs. In addition, stem cells undergo genetic mutations, which reduce their quality and effectiveness at renovating and repairing your body.

Imagine your stem cells as a team of repairmen in your newly constructed mansion. When the mansion is new and the repairmen are young, they can fix everything perfectly. But as the repairmen age and reduce in number, your mansion eventually goes into disrepair and finally crumbles.

What if you could restore and rejuvenate your stem cell population?

One option to accomplish this restoration and rejuvenation is to extract and concentrate your own autologous adult stem cells from places like your adipose (or fat) tissue or bone marrow.

These stem cells, however, are fewer in number and have undergone mutations (depending on your age) from their original ‘software code.’ Many scientists and physicians now prefer an alternative source, obtaining stem cells from the placenta or umbilical cord, the leftovers of birth.

These stem cells, available in large supply and expressing the undamaged software of a newborn, can be injected into joints or administered intravenously to rejuvenate and revitalize.

Think of these stem cells as chemical factories generating vital growth factors that can help to reduce inflammation, fight autoimmune disease, increase muscle mass, repair joints, and even revitalize skin and grow hair.

Over the last decade, the number of publications per year on stem cell-related research has increased 40x, and the stem cell market is expected to increase to $297 billion by 2022.

Rising research and development initiatives to develop therapeutic options for chronic diseases and growing demand for regenerative treatment options are the most significant drivers of this budding industry.

Biologists led by Kohji Nishida at Osaka University in Japan have discovered a new way to nurture and grow the tissues that make up the human eyeball. The scientists are able to grow retinas, corneas, the eye’s lens, and more, using only a small sample of adult skin.

In a Stanford study, seven of 18 stroke victims who agreed to stem cell treatments showed remarkable motor function improvements. This treatment could work for other neurodegenerative conditions such as Alzheimer’s, Parkinson’s, and ALS.

Doctors from the USC Neurorestoration Center and Keck Medicine of USC injected stem cells into the damaged cervical spine of a recently paralyzed 21-year-old man. Three months later, he showed dramatic improvement in sensation and movement of both arms.

In 2019, doctors in the U.K. cured a patient with HIV for the second time ever thanks to the efficacy of stem cells. After giving the cancer patient (who also had HIV) an allogeneic haematopoietic (e.g. blood) stem cell treatment for his Hodgkin’s lymphoma, the patient went into long-term HIV remission—18 months and counting at the time of the study’s publication.

Replace: Organ Regeneration and 3D Printing
Every 10 minutes, someone is added to the US organ transplant waiting list, totaling over 113,000 people waiting for replacement organs as of January 2019.

Countless more people in need of ‘spare parts’ never make it onto the waiting list. And on average, 20 people die each day while waiting for a transplant.

As a result, 35 percent of all US deaths (~900,000 people) could be prevented or delayed with access to organ replacements.

The excessive demand for donated organs will only intensify as technologies like self-driving cars make the world safer, given that many organ donors result from auto and motorcycle accidents. Safer vehicles mean less accidents and donations.

Clearly, replacement and regenerative medicine represent a massive opportunity.

Organ Entrepreneurs
Enter United Therapeutics CEO, Dr. Martine Rothblatt. A one-time aerospace entrepreneur (she was the founder of Sirius Satellite Radio), Rothblatt changed careers in the 1990s after her daughter developed a rare lung disease.

Her moonshot today is to create an industry of replacement organs. With an initial focus on diseases of the lung, Rothblatt set out to create replacement lungs. To accomplish this goal, her company United Therapeutics has pursued a number of technologies in parallel.

3D Printing Lungs
In 2017, United teamed up with one of the world’s largest 3D printing companies, 3D Systems, to build a collagen bioprinter and is paying another company, 3Scan, to slice up lungs and create detailed maps of their interior.

This 3D Systems bioprinter now operates according to a method called stereolithography. A UV laser flickers through a shallow pool of collagen doped with photosensitive molecules. Wherever the laser lingers, the collagen cures and becomes solid.

Gradually, the object being printed is lowered and new layers are added. The printer can currently lay down collagen at a resolution of around 20 micrometers, but will need to achieve resolution of a micrometer in size to make the lung functional.

Once a collagen lung scaffold has been printed, the next step is to infuse it with human cells, a process called recellularization.

The goal here is to use stem cells that grow on scaffolding and differentiate, ultimately providing the proper functionality. Early evidence indicates this approach can work.

In 2018, Harvard University experimental surgeon Harald Ott reported that he pumped billions of human cells (from umbilical cords and diced lungs) into a pig lung stripped of its own cells. When Ott’s team reconnected it to a pig’s circulation, the resulting organ showed rudimentary function.

Humanizing Pig Lungs
Another of Rothblatt’s organ manufacturing strategies is called xenotransplantation, the idea of transplanting an animal’s organs into humans who need a replacement.

Given the fact that adult pig organs are similar in size and shape to those of humans, United Therapeutics has focused on genetically engineering pigs to allow humans to use their organs. “It’s actually not rocket science,” said Rothblatt in her 2015 TED talk. “It’s editing one gene after another.”

To accomplish this goal, United Therapeutics made a series of investments in companies such as Revivicor Inc. and Synthetic Genomics Inc., and signed large funding agreements with the University of Maryland, University of Alabama, and New York Presbyterian/Columbia University Medical Center to create xenotransplantation programs for new hearts, kidneys, and lungs, respectively. Rothblatt hopes to see human translation in three to four years.

In preparation for that day, United Therapeutics owns a 132-acre property in Research Triangle Park and built a 275,000-square-foot medical laboratory that will ultimately have the capability to annually produce up to 1,000 sets of healthy pig lungs—known as xenolungs—from genetically engineered pigs.

Lung Ex Vivo Perfusion Systems
Beyond 3D printing and genetically engineering pig lungs, Rothblatt has already begun implementing a third near-term approach to improve the supply of lungs across the US.

Only about 30 percent of potential donor lungs meet transplant criteria in the first place; of those, only about 85 percent of those are usable once they arrive at the surgery center. As a result, nearly 75 percent of possible lungs never make it to the recipient in need.

What if these lungs could be rejuvenated? This concept informs Dr. Rothblatt’s next approach.

In 2016, United Therapeutics invested $41.8 million in TransMedics Inc., an Andover, Massachusetts company that develops ex vivo perfusion systems for donor lungs, hearts, and kidneys.

The XVIVO Perfusion System takes marginal-quality lungs that initially failed to meet transplantation standard-of-care criteria and perfuses and ventilates them at normothermic conditions, providing an opportunity for surgeons to reassess transplant suitability.

Rejuvenate Young Blood and Parabiosis
In HBO’s parody of the Bay Area tech community, Silicon Valley, one of the episodes (Season 4, Episode 5) is named “The Blood Boy.”

In this installment, tech billionaire Gavin Belson (Matt Ross) is meeting with Richard Hendricks (Thomas Middleditch) and his team, speaking about the future of the decentralized internet. A young, muscled twenty-something disrupts the meeting when he rolls in a transfusion stand and silently hooks an intravenous connection between himself and Belson.

Belson then introduces the newcomer as his “transfusion associate” and begins to explain the science of parabiosis: “Regular transfusions of the blood of a younger physically fit donor can significantly retard the aging process.”

While the sitcom is fiction, that science has merit, and the scenario portrayed in the episode is already happening today.

On the first point, research at Stanford and Harvard has demonstrated that older animals, when transfused with the blood of young animals, experience regeneration across many tissues and organs.

The opposite is also true: young animals, when transfused with the blood of older animals, experience accelerated aging. But capitalizing on this virtual fountain of youth has been tricky.

Ambrosia
One company, a San Francisco-based startup called Ambrosia, recently commenced one of the trials on parabiosis. Their protocol is simple: Healthy participants aged 35 and older get a transfusion of blood plasma from donors under 25, and researchers monitor their blood over the next two years for molecular indicators of health and aging.

Ambrosia’s founder Jesse Karmazin became interested in launching a company around parabiosis after seeing impressive data from animals and studies conducted abroad in humans: In one trial after another, subjects experience a reversal of aging symptoms across every major organ system. “The effects seem to be almost permanent,” he said. “It’s almost like there’s a resetting of gene expression.”

Infusing your own cord blood stem cells as you age may have tremendous longevity benefits. Following an FDA press release in February 2019, Ambrosia halted its consumer-facing treatment after several months of operation.

Understandably, the FDA raised concerns about the practice of parabiosis because to date, there is a marked lack of clinical data to support the treatment’s effectiveness.

Elevian
On the other end of the reputability spectrum is a startup called Elevian, spun out of Harvard University. Elevian is approaching longevity with a careful, scientifically validated strategy. (Full Disclosure: I am both an advisor to and investor in Elevian.)

CEO Mark Allen, MD, is joined by a dozen MDs and Ph.Ds out of Harvard. Elevian’s scientific founders started the company after identifying specific circulating factors that may be responsible for the “young blood” effect.

One example: A naturally occurring molecule known as “growth differentiation factor 11,” or GDF11, when injected into aged mice, reproduces many of the regenerative effects of young blood, regenerating heart, brain, muscles, lungs, and kidneys.

More specifically, GDF11 supplementation reduces age-related cardiac hypertrophy, accelerates skeletal muscle repair, improves exercise capacity, improves brain function and cerebral blood flow, and improves metabolism.

Elevian is developing a number of therapeutics that regulate GDF11 and other circulating factors. The goal is to restore our body’s natural regenerative capacity, which Elevian believes can address some of the root causes of age-associated disease with the promise of reversing or preventing many aging-related diseases and extending the healthy lifespan.

Conclusion
In 1992, futurist Leland Kaiser coined the term “regenerative medicine”:

“A new branch of medicine will develop that attempts to change the course of chronic disease and in many instances will regenerate tired and failing organ systems.”

Since then, the powerful regenerative medicine industry has grown exponentially, and this rapid growth is anticipated to continue.

A dramatic extension of the human healthspan is just over the horizon. Soon, we’ll all have the regenerative superpowers previously relegated to a handful of animals and comic books.

What new opportunities open up when anybody, anywhere, and at anytime can regenerate, replenish, and replace entire organs and metabolic systems on command?

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Posted in Human Robots

#434701 3 Practical Solutions to Offset ...

In recent years, the media has sounded the alarm about mass job loss to automation and robotics—some studies predict that up to 50 percent of current jobs or tasks could be automated in coming decades. While this topic has received significant attention, much of the press focuses on potential problems without proposing realistic solutions or considering new opportunities.

The economic impacts of AI, robotics, and automation are complex topics that require a more comprehensive perspective to understand. Is universal basic income, for example, the answer? Many believe so, and there are a number of experiments in progress. But it’s only one strategy, and without a sustainable funding source, universal basic income may not be practical.

As automation continues to accelerate, we’ll need a multi-pronged approach to ease the transition. In short, we need to update broad socioeconomic strategies for a new century of rapid progress. How, then, do we plan practical solutions to support these new strategies?

Take history as a rough guide to the future. Looking back, technology revolutions have three themes in common.

First, past revolutions each produced profound benefits to productivity, increasing human welfare. Second, technological innovation and technology diffusion have accelerated over time, each iteration placing more strain on the human ability to adapt. And third, machines have gradually replaced more elements of human work, with human societies adapting by moving into new forms of work—from agriculture to manufacturing to service, for example.

Public and private solutions, therefore, need to be developed to address each of these three components of change. Let’s explore some practical solutions for each in turn.

Figure 1. Technology’s structural impacts in the 21st century. Refer to Appendix I for quantitative charts and technological examples corresponding to the numbers (1-22) in each slice.
Solution 1: Capture New Opportunities Through Aggressive Investment
The rapid emergence of new technology promises a bounty of opportunity for the twenty-first century’s economic winners. This technological arms race is shaping up to be a global affair, and the winners will be determined in part by who is able to build the future economy fastest and most effectively. Both the private and public sectors have a role to play in stimulating growth.

At the country level, several nations have created competitive strategies to promote research and development investments as automation technologies become more mature.

Germany and China have two of the most notable growth strategies. Germany’s Industrie 4.0 plan targets a 50 percent increase in manufacturing productivity via digital initiatives, while halving the resources required. China’s Made in China 2025 national strategy sets ambitious targets and provides subsidies for domestic innovation and production. It also includes building new concept cities, investing in robotics capabilities, and subsidizing high-tech acquisitions abroad to become the leader in certain high-tech industries. For China, specifically, tech innovation is driven partially by a fear that technology will disrupt social structures and government control.

Such opportunities are not limited to existing economic powers. Estonia’s progress after the breakup of the Soviet Union is a good case study in transitioning to a digital economy. The nation rapidly implemented capitalistic reforms and transformed itself into a technology-centric economy in preparation for a massive tech disruption. Internet access was declared a right in 2000, and the country’s classrooms were outfitted for a digital economy, with coding as a core educational requirement starting at kindergarten. Internet broadband speeds in Estonia are among the fastest in the world. Accordingly, the World Bank now ranks Estonia as a high-income country.

Solution 2: Address Increased Rate of Change With More Nimble Education Systems
Education and training are currently not set for the speed of change in the modern economy. Schools are still based on a one-time education model, with school providing the foundation for a single lifelong career. With content becoming obsolete faster and rapidly escalating costs, this system may be unsustainable in the future. To help workers more smoothly transition from one job into another, for example, we need to make education a more nimble, lifelong endeavor.

Primary and university education may still have a role in training foundational thinking and general education, but it will be necessary to curtail rising price of tuition and increase accessibility. Massive open online courses (MooCs) and open-enrollment platforms are early demonstrations of what the future of general education may look like: cheap, effective, and flexible.

Georgia Tech’s online Engineering Master’s program (a fraction of the cost of residential tuition) is an early example in making university education more broadly available. Similarly, nanodegrees or microcredentials provided by online education platforms such as Udacity and Coursera can be used for mid-career adjustments at low cost. AI itself may be deployed to supplement the learning process, with applications such as AI-enhanced tutorials or personalized content recommendations backed by machine learning. Recent developments in neuroscience research could optimize this experience by perfectly tailoring content and delivery to the learner’s brain to maximize retention.

Finally, companies looking for more customized skills may take a larger role in education, providing on-the-job training for specific capabilities. One potential model involves partnering with community colleges to create apprenticeship-style learning, where students work part-time in parallel with their education. Siemens has pioneered such a model in four states and is developing a playbook for other companies to do the same.

Solution 3: Enhance Social Safety Nets to Smooth Automation Impacts
If predicted job losses to automation come to fruition, modernizing existing social safety nets will increasingly become a priority. While the issue of safety nets can become quickly politicized, it is worth noting that each prior technological revolution has come with corresponding changes to the social contract (see below).

The evolving social contract (U.S. examples)
– 1842 | Right to strike
– 1924 | Abolish child labor
– 1935 | Right to unionize
– 1938 | 40-hour work week
– 1962, 1974 | Trade adjustment assistance
– 1964 | Pay discrimination prohibited
– 1970 | Health and safety laws
– 21st century | AI and automation adjustment assistance?

Figure 2. Labor laws have historically adjusted as technology and society progressed

Solutions like universal basic income (no-strings-attached monthly payout to all citizens) are appealing in concept, but somewhat difficult to implement as a first measure in countries such as the US or Japan that already have high debt. Additionally, universal basic income may create dis-incentives to stay in the labor force. A similar cautionary tale in program design was the Trade Adjustment Assistance (TAA), which was designed to protect industries and workers from import competition shocks from globalization, but is viewed as a missed opportunity due to insufficient coverage.

A near-term solution could come in the form of graduated wage insurance (compensation for those forced to take a lower-paying job), including health insurance subsidies to individuals directly impacted by automation, with incentives to return to the workforce quickly. Another topic to tackle is geographic mismatch between workers and jobs, which can be addressed by mobility assistance. Lastly, a training stipend can be issued to individuals as means to upskill.

Policymakers can intervene to reverse recent historical trends that have shifted incomes from labor to capital owners. The balance could be shifted back to labor by placing higher taxes on capital—an example is the recently proposed “robot tax” where the taxation would be on the work rather than the individual executing it. That is, if a self-driving car performs the task that formerly was done by a human, the rideshare company will still pay the tax as if a human was driving.

Other solutions may involve distribution of work. Some countries, such as France and Sweden, have experimented with redistributing working hours. The idea is to cap weekly hours, with the goal of having more people employed and work more evenly spread. So far these programs have had mixed results, with lower unemployment but high costs to taxpayers, but are potential models that can continue to be tested.

We cannot stop growth, nor should we. With the roles in response to this evolution shifting, so should the social contract between the stakeholders. Government will continue to play a critical role as a stabilizing “thumb” in the invisible hand of capitalism, regulating and cushioning against extreme volatility, particularly in labor markets.

However, we already see business leaders taking on some of the role traditionally played by government—thinking about measures to remedy risks of climate change or economic proposals to combat unemployment—in part because of greater agility in adapting to change. Cross-disciplinary collaboration and creative solutions from all parties will be critical in crafting the future economy.

Note: The full paper this article is based on is available here.

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Posted in Human Robots

#434673 The World’s Most Valuable AI ...

It recognizes our faces. It knows the videos we might like. And it can even, perhaps, recommend the best course of action to take to maximize our personal health.

Artificial intelligence and its subset of disciplines—such as machine learning, natural language processing, and computer vision—are seemingly becoming integrated into our daily lives whether we like it or not. What was once sci-fi is now ubiquitous research and development in company and university labs around the world.

Similarly, the startups working on many of these AI technologies have seen their proverbial stock rise. More than 30 of these companies are now valued at over a billion dollars, according to data research firm CB Insights, which itself employs algorithms to provide insights into the tech business world.

Private companies with a billion-dollar valuation were so uncommon not that long ago that they were dubbed unicorns. Now there are 325 of these once-rare creatures, with a combined valuation north of a trillion dollars, as CB Insights maintains a running count of this exclusive Unicorn Club.

The subset of AI startups accounts for about 10 percent of the total membership, growing rapidly in just 4 years from 0 to 32. Last year, an unprecedented 17 AI startups broke the billion-dollar barrier, with 2018 also a record year for venture capital into private US AI companies at $9.3 billion, CB Insights reported.

What exactly is all this money funding?

AI Keeps an Eye Out for You
Let’s start with the bad news first.

Facial recognition is probably one of the most ubiquitous applications of AI today. It’s actually a decades-old technology often credited to a man named Woodrow Bledsoe, who used an instrument called a RAND tablet that could semi-autonomously match faces from a database. That was in the 1960s.

Today, most of us are familiar with facial recognition as a way to unlock our smartphones. But the technology has gained notoriety as a surveillance tool of law enforcement, particularly in China.

It’s no secret that the facial recognition algorithms developed by several of the AI unicorns from China—SenseTime, CloudWalk, and Face++ (also known as Megvii)—are used to monitor the country’s 1.3 billion citizens. Police there are even equipped with AI-powered eyeglasses for such purposes.

A fourth billion-dollar Chinese startup, Yitu Technologies, also produces a platform for facial recognition in the security realm, and develops AI systems in healthcare on top of that. For example, its CARE.AITM Intelligent 4D Imaging System for Chest CT can reputedly identify in real time a variety of lesions for the possible early detection of cancer.

The AI Doctor Is In
As Peter Diamandis recently noted, AI is rapidly augmenting healthcare and longevity. He mentioned another AI unicorn from China in this regard—iCarbonX, which plans to use machines to develop personalized health plans for every individual.

A couple of AI unicorns on the hardware side of healthcare are OrCam Technologies and Butterfly. The former, an Israeli company, has developed a wearable device for the vision impaired called MyEye that attaches to one’s eyeglasses. The device can identify people and products, as well as read text, conveying the information through discrete audio.

Butterfly Network, out of Connecticut, has completely upended the healthcare market with a handheld ultrasound machine that works with a smartphone.

“Orcam and Butterfly are amazing examples of how machine learning can be integrated into solutions that provide a step-function improvement over state of the art in ultra-competitive markets,” noted Andrew Byrnes, investment director at Comet Labs, a venture capital firm focused on AI and robotics, in an email exchange with Singularity Hub.

AI in the Driver’s Seat
Comet Labs’ portfolio includes two AI unicorns, Megvii and Pony.ai.

The latter is one of three billion-dollar startups developing the AI technology behind self-driving cars, with the other two being Momenta.ai and Zoox.

Founded in 2016 near San Francisco (with another headquarters in China), Pony.ai debuted its latest self-driving system, called PonyAlpha, last year. The platform uses multiple sensors (LiDAR, cameras, and radar) to navigate its environment, but its “sensor fusion technology” makes things simple by choosing the most reliable sensor data for any given driving scenario.

Zoox is another San Francisco area startup founded a couple of years earlier. In late 2018, it got the green light from the state of California to be the first autonomous vehicle company to transport a passenger as part of a pilot program. Meanwhile, China-based Momenta.ai is testing level four autonomy for its self-driving system. Autonomous driving levels are ranked zero to five, with level five being equal to a human behind the wheel.

The hype around autonomous driving is currently in overdrive, and Byrnes thinks regulatory roadblocks will keep most self-driving cars in idle for the foreseeable future. The exception, he said, is China, which is adopting a “systems” approach to autonomy for passenger transport.

“If [autonomous mobility] solves bigger problems like traffic that can elicit government backing, then that has the potential to go big fast,” he said. “This is why we believe Pony.ai will be a winner in the space.”

AI in the Back Office
An AI-powered technology that perhaps only fans of the cult classic Office Space might appreciate has suddenly taken the business world by storm—robotic process automation (RPA).

RPA companies take the mundane back office work, such as filling out invoices or processing insurance claims, and turn it over to bots. The intelligent part comes into play because these bots can tackle unstructured data, such as text in an email or even video and pictures, in order to accomplish an increasing variety of tasks.

Both Automation Anywhere and UiPath are older companies, founded in 2003 and 2005, respectively. However, since just 2017, they have raised nearly a combined $1 billion in disclosed capital.

Cybersecurity Embraces AI
Cybersecurity is another industry where AI is driving investment into startups. Sporting imposing names like CrowdStrike, Darktrace, and Tanium, these cybersecurity companies employ different machine-learning techniques to protect computers and other IT assets beyond the latest software update or virus scan.

Darktrace, for instance, takes its inspiration from the human immune system. Its algorithms can purportedly “learn” the unique pattern of each device and user on a network, detecting emerging problems before things spin out of control.

All three companies are used by major corporations and governments around the world. CrowdStrike itself made headlines a few years ago when it linked the hacking of the Democratic National Committee email servers to the Russian government.

Looking Forward
I could go on, and introduce you to the world’s most valuable startup, a Chinese company called Bytedance that is valued at $75 billion for news curation and an app to create 15-second viral videos. But that’s probably not where VC firms like Comet Labs are generally putting their money.

Byrnes sees real value in startups that are taking “data-driven approaches to problems specific to unique industries.” Take the example of Chicago-based unicorn Uptake Technologies, which analyzes incoming data from machines, from wind turbines to tractors, to predict problems before they occur with the machinery. A not-yet unicorn called PingThings in the Comet Labs portfolio does similar predictive analytics for the energy utilities sector.

“One question we like asking is, ‘What does the state of the art look like in your industry in three to five years?’” Byrnes said. “We ask that a lot, then we go out and find the technology-focused teams building those things.”

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Posted in Human Robots

#434658 The Next Data-Driven Healthtech ...

Increasing your healthspan (i.e. making 100 years old the new 60) will depend to a large degree on artificial intelligence. And, as we saw in last week’s blog, healthcare AI systems are extremely data-hungry.

Fortunately, a slew of new sensors and data acquisition methods—including over 122 million wearables shipped in 2018—are bursting onto the scene to meet the massive demand for medical data.

From ubiquitous biosensors, to the mobile healthcare revolution, to the transformative power of the Health Nucleus, converging exponential technologies are fundamentally transforming our approach to healthcare.

In Part 4 of this blog series on Longevity & Vitality, I expand on how we’re acquiring the data to fuel today’s AI healthcare revolution.

In this blog, I’ll explore:

How the Health Nucleus is transforming “sick care” to healthcare
Sensors, wearables, and nanobots
The advent of mobile health

Let’s dive in.

Health Nucleus: Transforming ‘Sick Care’ to Healthcare
Much of today’s healthcare system is actually sick care. Most of us assume that we’re perfectly healthy, with nothing going on inside our bodies, until the day we travel to the hospital writhing in pain only to discover a serious or life-threatening condition.

Chances are that your ailment didn’t materialize that morning; rather, it’s been growing or developing for some time. You simply weren’t aware of it. At that point, once you’re diagnosed as “sick,” our medical system engages to take care of you.

What if, instead of this retrospective and reactive approach, you were constantly monitored, so that you could know the moment anything was out of whack?

Better yet, what if you more closely monitored those aspects of your body that your gene sequence predicted might cause you difficulty? Think: your heart, your kidneys, your breasts. Such a system becomes personalized, predictive, and possibly preventative.

This is the mission of the Health Nucleus platform built by Human Longevity, Inc. (HLI). While not continuous—that will come later, with the next generation of wearable and implantable sensors—the Health Nucleus was designed to ‘digitize’ you once per year to help you determine whether anything is going on inside your body that requires immediate attention.

The Health Nucleus visit provides you with the following tests during a half-day visit:

Whole genome sequencing (30x coverage)
Whole body (non-contrast) MRI
Brain magnetic resonance imaging/angiography (MRI/MRA)
CT (computed tomography) of the heart and lungs
Coronary artery calcium scoring
Electrocardiogram
Echocardiogram
Continuous cardiac monitoring
Clinical laboratory tests and metabolomics

In late 2018, HLI published the results of the first 1,190 clients through the Health Nucleus. The results were eye-opening—especially since these patients were all financially well-off, and already had access to the best doctors.

Following are the physiological and genomic findings in these clients who self-selected to undergo evaluation at HLI’s Health Nucleus.

Physiological Findings [TG]

Two percent had previously unknown tumors detected by MRI
2.5 percent had previously undetected aneurysms detected by MRI
Eight percent had cardiac arrhythmia found on cardiac rhythm monitoring, not previously known
Nine percent had moderate-severe coronary artery disease risk, not previously known
16 percent discovered previously unknown cardiac structure/function abnormalities
30 percent had elevated liver fat, not previously known

Genomic Findings [TG]

24 percent of clients uncovered a rare (unknown) genetic mutation found on WGS
63 percent of clients had a rare genetic mutation with a corresponding phenotypic finding

In summary, HLI’s published results found that 14.4 percent of clients had significant findings that are actionable, requiring immediate or near-term follow-up and intervention.

Long-term value findings were found in 40 percent of the clients we screened. Long-term clinical findings include discoveries that require medical attention or monitoring but are not immediately life-threatening.

The bottom line: most people truly don’t know their actual state of health. The ability to take a fully digital deep dive into your health status at least once per year will enable you to detect disease at stage zero or stage one, when it is most curable.

Sensors, Wearables, and Nanobots
Wearables, connected devices, and quantified self apps will allow us to continuously collect enormous amounts of useful health information.

Wearables like the Quanttus wristband and Vital Connect can transmit your electrocardiogram data, vital signs, posture, and stress levels anywhere on the planet.

In April 2017, we were proud to grant $2.5 million in prize money to the winning team in the Qualcomm Tricorder XPRIZE, Final Frontier Medical Devices.

Using a group of noninvasive sensors that collect data on vital signs, body chemistry, and biological functions, Final Frontier integrates this data in their powerful, AI-based DxtER diagnostic engine for rapid, high-precision assessments.

Their engine combines learnings from clinical emergency medicine and data analysis from actual patients.

Google is developing a full range of internal and external sensors (e.g. smart contact lenses) that can monitor the wearer’s vitals, ranging from blood sugar levels to blood chemistry.

In September 2018, Apple announced its Series 4 Apple Watch, including an FDA-approved mobile, on-the-fly ECG. Granted its first FDA approval, Apple appears to be moving deeper into the sensing healthcare market.

Further, Apple is reportedly now developing sensors that can non-invasively monitor blood sugar levels in real time for diabetic treatment. IoT-connected sensors are also entering the world of prescription drugs.

Last year, the FDA approved the first sensor-embedded pill, Abilify MyCite. This new class of digital pills can now communicate medication data to a user-controlled app, to which doctors may be granted access for remote monitoring.

Perhaps what is most impressive about the next generation of wearables and implantables is the density of sensors, processing, networking, and battery capability that we can now cheaply and compactly integrate.

Take the second-generation OURA ring, for example, which focuses on sleep measurement and management.

The OURA ring looks like a slightly thick wedding band, yet contains an impressive array of sensors and capabilities, including:

Two infrared LED
One infrared sensor
Three temperature sensors
One accelerometer
A six-axis gyro
A curved battery with a seven-day life
The memory, processing, and transmission capability required to connect with your smartphone

Disrupting Medical Imaging Hardware
In 2018, we saw lab breakthroughs that will drive the cost of an ultrasound sensor to below $100, in a packaging smaller than most bandages, powered by a smartphone. Dramatically disrupting ultrasound is just the beginning.

Nanobots and Nanonetworks
While wearables have long been able to track and transmit our steps, heart rate, and other health data, smart nanobots and ingestible sensors will soon be able to monitor countless new parameters and even help diagnose disease.

Some of the most exciting breakthroughs in smart nanotechnology from the past year include:

Researchers from the École Polytechnique Fédérale de Lausanne (EPFL) and the Swiss Federal Institute of Technology in Zurich (ETH Zurich) demonstrated artificial microrobots that can swim and navigate through different fluids, independent of additional sensors, electronics, or power transmission.

Researchers at the University of Chicago proposed specific arrangements of DNA-based molecular logic gates to capture the information contained in the temporal portion of our cells’ communication mechanisms. Accessing the otherwise-lost time-dependent information of these cellular signals is akin to knowing the tune of a song, rather than solely the lyrics.

MIT researchers built micron-scale robots able to sense, record, and store information about their environment. These tiny robots, about 100 micrometers in diameter (approximately the size of a human egg cell), can also carry out pre-programmed computational tasks.

Engineers at University of California, San Diego developed ultrasound-powered nanorobots that swim efficiently through your blood, removing harmful bacteria and the toxins they produce.

But it doesn’t stop there.

As nanosensor and nanonetworking capabilities develop, these tiny bots may soon communicate with each other, enabling the targeted delivery of drugs and autonomous corrective action.

Mobile Health
The OURA ring and the Series 4 Apple Watch are just the tip of the spear when it comes to our future of mobile health. This field, predicted to become a $102 billion market by 2022, puts an on-demand virtual doctor in your back pocket.

Step aside, WebMD.

In true exponential technology fashion, mobile device penetration has increased dramatically, while image recognition error rates and sensor costs have sharply declined.

As a result, AI-powered medical chatbots are flooding the market; diagnostic apps can identify anything from a rash to diabetic retinopathy; and with the advent of global connectivity, mHealth platforms enable real-time health data collection, transmission, and remote diagnosis by medical professionals.

Already available to residents across North London, Babylon Health offers immediate medical advice through AI-powered chatbots and video consultations with doctors via its app.

Babylon now aims to build up its AI for advanced diagnostics and even prescription. Others, like Woebot, take on mental health, using cognitive behavioral therapy in communications over Facebook messenger with patients suffering from depression.

In addition to phone apps and add-ons that test for fertility or autism, the now-FDA-approved Clarius L7 Linear Array Ultrasound Scanner can connect directly to iOS and Android devices and perform wireless ultrasounds at a moment’s notice.

Next, Healthy.io, an Israeli startup, uses your smartphone and computer vision to analyze traditional urine test strips—all you need to do is take a few photos.

With mHealth platforms like ClickMedix, which connects remotely-located patients to medical providers through real-time health data collection and transmission, what’s to stop us from delivering needed treatments through drone delivery or robotic telesurgery?

Welcome to the age of smartphone-as-a-medical-device.

Conclusion
With these DIY data collection and diagnostic tools, we save on transportation costs (time and money), and time bottlenecks.

No longer will you need to wait for your urine or blood results to go through the current information chain: samples will be sent to the lab, analyzed by a technician, results interpreted by your doctor, and only then relayed to you.

Just like the “sage-on-the-stage” issue with today’s education system, healthcare has a “doctor-on-the-dais” problem. Current medical procedures are too complicated and expensive for a layperson to perform and analyze on their own.

The coming abundance of healthcare data promises to transform how we approach healthcare, putting the power of exponential technologies in the patient’s hands and revolutionizing how we live.

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#434648 The Pediatric AI That Outperformed ...

Training a doctor takes years of grueling work in universities and hospitals. Building a doctor may be as easy as teaching an AI how to read.

Artificial intelligence has taken another step towards becoming an integral part of 21st-century medicine. New research out of Guangzhou, China, published February 11th in Nature Medicine Letters, has demonstrated a natural-language processing AI that is capable of out-performing rookie pediatricians in diagnosing common childhood ailments.

The massive study examined the electronic health records (EHR) from nearly 600,000 patients over an 18-month period at the Guangzhou Women and Children’s Medical Center and then compared AI-generated diagnoses against new assessments from physicians with a range of experience.

The verdict? On average, the AI was noticeably more accurate than junior physicians and nearly as reliable as the more senior ones. These results are the latest demonstration that artificial intelligence is on the cusp of becoming a healthcare staple on a global scale.

Less Like a Computer, More Like a Person
To outshine human doctors, the AI first had to become more human. Like IBM’s Watson, the pediatric AI leverages natural language processing, in essence “reading” written notes from EHRs not unlike how a human doctor would review those same records. But the similarities to human doctors don’t end there. The AI is a machine learning classifier (MLC), capable of placing the information learned from the EHRs into categories to improve performance.

Like traditionally-trained pediatricians, the AI broke cases down into major organ groups and infection areas (upper/lower respiratory, gastrointestinal, etc.) before breaking them down even further into subcategories. It could then develop associations between various symptoms and organ groups and use those associations to improve its diagnoses. This hierarchical approach mimics the deductive reasoning human doctors employ.

Another key strength of the AI developed for this study was the enormous size of the dataset collected to teach it: 1,362,559 outpatient visits from 567,498 patients yielded some 101.6 million data points for the MLC to devour on its quest for pediatric dominance. This allowed the AI the depth of learning needed to distinguish and accurately select from the 55 different diagnosis codes across the various organ groups and subcategories.

When comparing against the human doctors, the study used 11,926 records from an unrelated group of children, giving both the MLC and the 20 humans it was compared against an even playing field. The results were clear: while cohorts of senior pediatricians performed better than the AI, junior pediatricians (those with 3-15 years of experience) were outclassed.

Helping, Not Replacing
While the research used a competitive analysis to measure the success of the AI, the results should be seen as anything but hostile to human doctors. The near future of artificial intelligence in medicine will see these machine learning programs augment, not replace, human physicians. The authors of the study specifically call out augmentation as the key short-term application of their work. Triaging incoming patients via intake forms, performing massive metastudies using EHRs, providing rapid ‘second opinions’—the applications for an AI doctor that is better-but-not-the-best are as varied as the healthcare industry itself.

That’s only considering how artificial intelligence could make a positive impact immediately upon implementation. It’s easy to see how long-term use of a diagnostic assistant could reshape the way modern medical institutions approach their work.

Look at how the MLC results fit snugly between the junior and senior physician groups. Essentially, it took nearly 15 years before a physician could consistently out-diagnose the machine. That’s a decade and a half wherein an AI diagnostic assistant would be an invaluable partner—both as a training tool and a safety measure. Likewise, on the other side of the experience curve you have physicians whose performance could be continuously leveraged to improve the AI’s effectiveness. This is a clear opportunity for a symbiotic relationship, with humans and machines each assisting the other as they mature.

Closer to Us, But Still Dependent on Us
No matter the ultimate application, the AI doctors of the future are drawing nearer to us step by step. This latest research is a demonstration that artificial intelligence can mimic the results of human deductive reasoning even in some of the most complex and important decision-making processes. True, the MLC required input from humans to function; both the initial data points and the cases used to evaluate the AI depended on EHRs written by physicians. While every effort was made to design a test schema that removed any indication of the eventual diagnosis, some “data leakage” is bound to occur.

In other words, when AIs use human-created data, they inherit human insight to some degree. Yet the progress made in machine imaging, chatbots, sensors, and other fields all suggest that this dependence on human input is more about where we are right now than where we could be in the near future.

Data, and More Data
That near future may also have some clear winners and losers. For now, those winners seem to be the institutions that can capture and apply the largest sets of data. With a rapidly digitized society gathering incredible amounts of data, China has a clear advantage. Combined with their relatively relaxed approach to privacy, they are likely to continue as one of the driving forces behind machine learning and its applications. So too will Google/Alphabet with their massive medical studies. Data is the uranium in this AI arms race, and everyone seems to be scrambling to collect more.

In a global community that seems increasingly aware of the potential problems arising from this need for and reliance on data, it’s nice to know there’ll be an upside as well. The technology behind AI medical assistants is looking more and more mature—even if we are still struggling to find exactly where, when, and how that technology should first become universal.

Yet wherever we see the next push to make AI a standard tool in a real-world medical setting, I have little doubt it will greatly improve the lives of human patients. Today Doctor AI is performing as well as a human colleague with more than 10 years of experience. By next year or so, it may take twice as long for humans to be competitive. And in a decade, the combined medical knowledge of all human history may be a tool as common as a stethoscope in your doctor’s hands.

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