The Algorithm Will See You Now
There's a quiet revolution going on in medicine. It's driven by artificial intelligence, but paradoxically, new technology may put a more human face on healthcare.
AI's usefulness in healthcare ranges far and wide.
Artificial intelligence is software that can process massive amounts of information and learn over time, arriving at decisions with striking accuracy and efficiency. It offers greater accuracy in diagnosis, exponentially faster genome sequencing, the mining of medical literature and patient records at breathtaking speed, a dramatic reduction in administrative bureaucracy, personalized medicine, and even the democratization of healthcare.
The algorithms that bring these advantages won't replace doctors; rather, by offloading some of the most time-consuming tasks in healthcare, providers will be able to focus on personal interactions with patients—listening, empathizing, educating and generally putting the care back in healthcare. The relationship can focus on the alleviation of suffering, both the physical and emotional kind.
Challenges of Getting AI Up and Running
The AI revolution, still in its early phase in medicine, is already spurring some amazing advances, despite the fact that some experts say it has been overhyped. IBM's Watson Health program is a case in point. IBM capitalized on Watson's ability to process natural language by designing algorithms that devour data like medical articles and analyze images like MRIs and medical slides. The algorithms help diagnose diseases and recommend treatment strategies.
But Technology Review reported that a heavily hyped partnership with the MD Anderson Cancer Center in Houston fell apart in 2017 because of a lack of data in the proper format. The data existed, just not in a way that the voraciously data-hungry AI could use to train itself.
The hiccup certainly hasn't dampened the enthusiasm for medical AI among other tech giants, including Google and Apple, both of which have invested billions in their own healthcare projects. At this point, the main challenge is the need for algorithms to interpret a huge diversity of data mined from medical records. This can include everything from CT scans, MRIs, electrocardiograms, x-rays, and medical slides, to millions of pages of medical literature, physician's notes, and patient histories. It can even include data from implantables and wearables such as the Apple Watch and blood sugar monitors.
None of this information is in anything resembling a standard format across and even within hospitals, clinics, and diagnostic centers. Once the algorithms are trained, however, they can crunch massive amounts of data at blinding speed, with an accuracy that matches and sometimes even exceeds that of highly experienced doctors.
Genome sequencing, for example, took years to accomplish as recently as the early 2000s. The Human Genome Project, the first sequencing of the human genome, was an international effort that took 13 years to complete. In April of this year, Rady Children's Institute for Genomic Medicine in San Diego used an AI-powered genome sequencing algorithm to diagnose rare genetic diseases in infants in about 20 hours, according to ScienceDaily.
"Patient care will always begin and end with the doctor."
Dr. Stephen Kingsmore, the lead author of an article published in Science Translational Medicine, emphasized that even though the algorithm helped guide the treatment strategies of neonatal intensive care physicians, the doctor was still an indispensable link in the chain. "Some people call this artificial intelligence, we call it augmented intelligence," he says. "Patient care will always begin and end with the doctor."
One existing trend is helping to supply a great amount of valuable data to algorithms—the electronic health record. Initially blamed for exacerbating the already crushing workload of many physicians, the EHR is emerging as a boon for algorithms because it consolidates all of a patient's data in one record.
Examples of AI in Action Around the Globe
If you're a parent who has ever taken a child to the doctor with flulike symptoms, you know the anxiety of wondering if the symptoms signal something serious. Kang Zhang, M.D., Ph.D., the founding director of the Institute for Genomic Medicine at the University of California at San Diego, and colleagues developed an AI natural language processing model that used deep learning to analyze the EHRs of 1.3 million pediatric visits to a clinic in Guanzhou, China.
The AI identified common childhood diseases with about the same accuracy as human doctors, and it was even able to split the diagnoses into two categories—common conditions such as flu, and serious, life-threatening conditions like meningitis. Zhang has emphasized that the algorithm didn't replace the human doctor, but it did streamline the diagnostic process and could be used in a triage capacity when emergency room personnel need to prioritize the seriously ill over those suffering from common, less dangerous ailments.
AI's usefulness in healthcare ranges far and wide. In Uganda and several other African nations, AI is bringing modern diagnostics to remote villages that have no access to traditional technologies such as x-rays. The New York Times recently reported that there, doctors are using a pocket-sized, hand-held ultrasound machine that works in concert with a cell phone to image and diagnose everything from pneumonia (a common killer of children) to cancerous tumors.
The beauty of the highly portable, battery-powered device is that ultrasound images can be uploaded on computers so that physicians anywhere in the world can review them and weigh in with their advice. And the images are instantly incorporated into the patient's EHR.
Jonathan Rothberg, the founder of Butterfly Network, the Connecticut company that makes the device, told The New York Times that "Two thirds of the world's population gets no imaging at all. When you put something on a chip, the price goes down and you democratize it." The Butterfly ultrasound machine, which sells for $2,000, promises to be a game-changer in remote areas of Africa, South America, and Asia, as well as at the bedsides of patients in developed countries.
AI algorithms are rapidly emerging in healthcare across the U.S. and the world. China has become a major international player, set to surpass the U.S. this year in AI capital investment, the translation of AI research into marketable products, and even the number of often-cited research papers on AI. So far the U.S. is still the leader, but some experts describe the relationship between the U.S. and China as an AI cold war.
"The future of machine learning isn't sentient killer robots. It's longer human lives."
The U.S. Food and Drug Administration expanded its approval of medical algorithms from two in all of 2017 to about two per month throughout 2018. One of the first fields to be impacted is ophthalmology.
One algorithm, developed by the British AI company DeepMind (owned by Alphabet, the parent company of Google), instantly scans patients' retinas and is able to diagnose diabetic retinopathy without needing an ophthalmologist to interpret the scans. This means diabetics can get the test every year from their family physician without having to see a specialist. The Financial Times reported in March that the technology is now being used in clinics throughout Europe.
In Copenhagen, emergency service dispatchers are using a new voice-processing AI called Corti to analyze the conversations in emergency phone calls. The algorithm analyzes the verbal cues of callers, searches its huge database of medical information, and provides dispatchers with onscreen diagnostic information. Freddy Lippert, the CEO of EMS Copenhagen, notes that the algorithm has already saved lives by expediting accurate diagnoses in high-pressure situations where time is of the essence.
Researchers at the University of Nottingham in the UK have even developed a deep learning algorithm that predicts death more accurately than human clinicians. The algorithm incorporates data from a huge range of factors in a chronically ill population, including how many fruits and vegetables a patient eats on a daily basis. Dr. Stephen Weng, lead author of the study, published in PLOS ONE, said in a press release, "We found machine learning algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert."
New digital technologies are allowing patients to participate in their healthcare as never before. A feature of the new Apple Watch is an app that detects cardiac arrhythmias and even produces an electrocardiogram if an abnormality is detected. The technology, approved by the FDA, is helping cardiologists monitor heart patients and design interventions for those who may be at higher risk of a cardiac event like a stroke.
If having an algorithm predict your death sends a shiver down your spine, consider that algorithms may keep you alive longer. In 2018, technology reporter Tristan Greene wrote for Medium that "…despite the unending deluge of panic-ridden articles declaring AI the path to apocalypse, we're now living in a world where algorithms save lives every day. The future of machine learning isn't sentient killer robots. It's longer human lives."
The Risks of AI Compiling Your Data
To be sure, the advent of AI-infused medical technology is not without its risks. One risk is that the use of AI wearables constantly monitoring our vital signs could turn us into a nation of hypochondriacs, racing to our doctors every time there's a blip in some vital sign. Such a development could stress an already overburdened system that suffers from, among other things, a shortage of doctors and nurses. Another risk has to do with the privacy protections on the massive repository of intimately personal information that AI will have on us.
In an article recently published in the Journal of the American Medical Association, Australian researcher Kit Huckvale and colleagues examined the handling of data by 36 smartphone apps that assisted people with either depression or smoking cessation, two areas that could lend themselves to stigmatization if they fell into the wrong hands.
Out of the 36 apps, 33 shared their data with third parties, despite the fact that just 25 of those apps had a privacy policy at all and out of those, only 23 stated that data would be shared with third parties. The recipients of all that data? It went almost exclusively to Facebook and Google, to be used for advertising and marketing purposes. But there's nothing to stop it from ending up in the hands of insurers, background databases, or any other entity.
Even when data isn't voluntarily shared, any digital information can be hacked. EHRs and even wearable devices share the same vulnerability as any other digital record or device. Still, the promise of AI to radically improve efficiency and accuracy in healthcare is hard to ignore.
AI Can Help Restore Humanity to Medicine
Eric Topol, director of the Scripps Research Translational Institute and author of the new book Deep Medicine, says that AI gives doctors and nurses the most precious gift of all: time.
Topol welcomes his patients' use of the Apple Watch cardiac feature and is optimistic about the ways that AI is revolutionizing medicine. He says that the watch helps doctors monitor how well medications are working and has already helped to prevent strokes. But in addition to that, AI will help bring the humanity back to a profession that has become as cold and hard as a stainless steel dissection table.
"When I graduated from medical school in the 1970s," he says, "you had a really intimate relationship with your doctor." Over the decades, he has seen that relationship steadily erode as medical organizations demanded that doctors see more and more patients within ever-shrinking time windows.
"Doctors have no time to think, to communicate. We need to restore the mission in medicine."
In addition to that, EHRs have meant that doctors and nurses are getting buried in paperwork and administrative tasks. This is no doubt one reason why a recent study by the World Health Organization showed that worldwide, about 50 percent of doctors suffer from burnout. People who are utterly exhausted make more mistakes, and medical clinicians are no different from the rest of us. Only medical mistakes have unacceptably high stakes. According to its website, Johns Hopkins University recently announced that in the U.S. alone, 250,000 people die from medical mistakes each year.
"Doctors have no time to think, to communicate," says Topol. "We need to restore the mission in medicine." AI is giving doctors more time to devote to the thing that attracted them to medicine in the first place—connecting deeply with patients.
There is a real danger at this juncture, though, that administrators aware of the time-saving aspects of AI will simply push doctors to see more patients, read more tests, and embrace an even more crushing workload.
"We can't leave it to the administrators to just make things worse," says Topol. "Now is the time for doctors to advocate for a restoration of the human touch. We need to stand up for patients and for the patient-doctor relationship."
AI could indeed be a game changer, he says, but rather than squander the huge benefits of more time, "We need a new equation going forward."
New elevators could lift up our access to space
Story by Big Think
When people first started exploring space in the 1960s, it cost upwards of $80,000 (adjusted for inflation) to put a single pound of payload into low-Earth orbit.
A major reason for this high cost was the need to build a new, expensive rocket for every launch. That really started to change when SpaceX began making cheap, reusable rockets, and today, the company is ferrying customer payloads to LEO at a price of just $1,300 per pound.
This is making space accessible to scientists, startups, and tourists who never could have afforded it previously, but the cheapest way to reach orbit might not be a rocket at all — it could be an elevator.
The space elevator
The seeds for a space elevator were first planted by Russian scientist Konstantin Tsiolkovsky in 1895, who, after visiting the 1,000-foot (305 m) Eiffel Tower, published a paper theorizing about the construction of a structure 22,000 miles (35,400 km) high.
This would provide access to geostationary orbit, an altitude where objects appear to remain fixed above Earth’s surface, but Tsiolkovsky conceded that no material could support the weight of such a tower.
We could then send electrically powered “climber” vehicles up and down the tether to deliver payloads to any Earth orbit.
In 1959, soon after Sputnik, Russian engineer Yuri N. Artsutanov proposed a way around this issue: instead of building a space elevator from the ground up, start at the top. More specifically, he suggested placing a satellite in geostationary orbit and dropping a tether from it down to Earth’s equator. As the tether descended, the satellite would ascend. Once attached to Earth’s surface, the tether would be kept taut, thanks to a combination of gravitational and centrifugal forces.
We could then send electrically powered “climber” vehicles up and down the tether to deliver payloads to any Earth orbit. According to physicist Bradley Edwards, who researched the concept for NASA about 20 years ago, it’d cost $10 billion and take 15 years to build a space elevator, but once operational, the cost of sending a payload to any Earth orbit could be as low as $100 per pound.
“Once you reduce the cost to almost a Fed-Ex kind of level, it opens the doors to lots of people, lots of countries, and lots of companies to get involved in space,” Edwards told Space.com in 2005.
In addition to the economic advantages, a space elevator would also be cleaner than using rockets — there’d be no burning of fuel, no harmful greenhouse emissions — and the new transport system wouldn’t contribute to the problem of space junk to the same degree that expendable rockets do.
So, why don’t we have one yet?
Tether troubles
Edwards wrote in his report for NASA that all of the technology needed to build a space elevator already existed except the material needed to build the tether, which needs to be light but also strong enough to withstand all the huge forces acting upon it.
The good news, according to the report, was that the perfect material — ultra-strong, ultra-tiny “nanotubes” of carbon — would be available in just two years.
“[S]teel is not strong enough, neither is Kevlar, carbon fiber, spider silk, or any other material other than carbon nanotubes,” wrote Edwards. “Fortunately for us, carbon nanotube research is extremely hot right now, and it is progressing quickly to commercial production.”Unfortunately, he misjudged how hard it would be to synthesize carbon nanotubes — to date, no one has been able to grow one longer than 21 inches (53 cm).
Further research into the material revealed that it tends to fray under extreme stress, too, meaning even if we could manufacture carbon nanotubes at the lengths needed, they’d be at risk of snapping, not only destroying the space elevator, but threatening lives on Earth.
Looking ahead
Carbon nanotubes might have been the early frontrunner as the tether material for space elevators, but there are other options, including graphene, an essentially two-dimensional form of carbon that is already easier to scale up than nanotubes (though still not easy).
Contrary to Edwards’ report, Johns Hopkins University researchers Sean Sun and Dan Popescu say Kevlar fibers could work — we would just need to constantly repair the tether, the same way the human body constantly repairs its tendons.
“Using sensors and artificially intelligent software, it would be possible to model the whole tether mathematically so as to predict when, where, and how the fibers would break,” the researchers wrote in Aeon in 2018.
“When they did, speedy robotic climbers patrolling up and down the tether would replace them, adjusting the rate of maintenance and repair as needed — mimicking the sensitivity of biological processes,” they continued.Astronomers from the University of Cambridge and Columbia University also think Kevlar could work for a space elevator — if we built it from the moon, rather than Earth.
They call their concept the Spaceline, and the idea is that a tether attached to the moon’s surface could extend toward Earth’s geostationary orbit, held taut by the pull of our planet’s gravity. We could then use rockets to deliver payloads — and potentially people — to solar-powered climber robots positioned at the end of this 200,000+ mile long tether. The bots could then travel up the line to the moon’s surface.
This wouldn’t eliminate the need for rockets to get into Earth’s orbit, but it would be a cheaper way to get to the moon. The forces acting on a lunar space elevator wouldn’t be as strong as one extending from Earth’s surface, either, according to the researchers, opening up more options for tether materials.
“[T]he necessary strength of the material is much lower than an Earth-based elevator — and thus it could be built from fibers that are already mass-produced … and relatively affordable,” they wrote in a paper shared on the preprint server arXiv.
After riding up the Earth-based space elevator, a capsule would fly to a space station attached to the tether of the moon-based one.
Electrically powered climber capsules could go up down the tether to deliver payloads to any Earth orbit.
Adobe Stock
Some Chinese researchers, meanwhile, aren’t giving up on the idea of using carbon nanotubes for a space elevator — in 2018, a team from Tsinghua University revealed that they’d developed nanotubes that they say are strong enough for a tether.
The researchers are still working on the issue of scaling up production, but in 2021, state-owned news outlet Xinhua released a video depicting an in-development concept, called “Sky Ladder,” that would consist of space elevators above Earth and the moon.
After riding up the Earth-based space elevator, a capsule would fly to a space station attached to the tether of the moon-based one. If the project could be pulled off — a huge if — China predicts Sky Ladder could cut the cost of sending people and goods to the moon by 96 percent.
The bottom line
In the 120 years since Tsiolkovsky looked at the Eiffel Tower and thought way bigger, tremendous progress has been made developing materials with the properties needed for a space elevator. At this point, it seems likely we could one day have a material that can be manufactured at the scale needed for a tether — but by the time that happens, the need for a space elevator may have evaporated.
Several aerospace companies are making progress with their own reusable rockets, and as those join the market with SpaceX, competition could cause launch prices to fall further.
California startup SpinLaunch, meanwhile, is developing a massive centrifuge to fling payloads into space, where much smaller rockets can propel them into orbit. If the company succeeds (another one of those big ifs), it says the system would slash the amount of fuel needed to reach orbit by 70 percent.
Even if SpinLaunch doesn’t get off the ground, several groups are developing environmentally friendly rocket fuels that produce far fewer (or no) harmful emissions. More work is needed to efficiently scale up their production, but overcoming that hurdle will likely be far easier than building a 22,000-mile (35,400-km) elevator to space.
New tech aims to make the ocean healthier for marine life
A defunct drydock basin arched by a rusting 19th century steel bridge seems an incongruous place to conduct state-of-the-art climate science. But this placid and protected sliver of water connecting Brooklyn’s Navy Yard to the East River was just right for Garrett Boudinot to float a small dock topped with water carbon-sensing gear. And while his system right now looks like a trio of plastic boxes wired up together, it aims to mediate the growing ocean acidification problem, caused by overabundance of dissolved carbon dioxide.
Boudinot, a biogeochemist and founder of a carbon-management startup called Vycarb, is honing his method for measuring CO2 levels in water, as well as (at least temporarily) correcting their negative effects. It’s a challenge that’s been occupying numerous climate scientists as the ocean heats up, and as states like New York recognize that reducing emissions won’t be enough to reach their climate goals; they’ll have to figure out how to remove carbon, too.
To date, though, methods for measuring CO2 in water at scale have been either intensely expensive, requiring fancy sensors that pump CO2 through membranes; or prohibitively complicated, involving a series of lab-based analyses. And that’s led to a bottleneck in efforts to remove carbon as well.
But recently, Boudinot cracked part of the code for measurement and mitigation, at least on a small scale. While the rest of the industry sorts out larger intricacies like getting ocean carbon markets up and running and driving carbon removal at billion-ton scale in centralized infrastructure, his decentralized method could have important, more immediate implications.
Specifically, for shellfish hatcheries, which grow seafood for human consumption and for coastal restoration projects. Some of these incubators for oysters and clams and scallops are already feeling the negative effects of excess carbon in water, and Vycarb’s tech could improve outcomes for the larval- and juvenile-stage mollusks they’re raising. “We’re learning from these folks about what their needs are, so that we’re developing our system as a solution that’s relevant,” Boudinot says.
Ocean acidification can wreak havoc on developing shellfish, inhibiting their shells from growing and leading to mass die-offs.
Ocean waters naturally absorb CO2 gas from the atmosphere. When CO2 accumulates faster than nature can dissipate it, it reacts with H2O molecules, forming carbonic acid, H2CO3, which makes the water column more acidic. On the West Coast, acidification occurs when deep, carbon dioxide-rich waters upwell onto the coast. This can wreak havoc on developing shellfish, inhibiting their shells from growing and leading to mass die-offs; this happened, disastrously, at Pacific Northwest oyster hatcheries in 2007.
This type of acidification will eventually come for the East Coast, too, says Ryan Wallace, assistant professor and graduate director of environmental studies and sciences at Long Island’s Adelphi University, who studies acidification. But at the moment, East Coast acidification has other sources: agricultural runoff, usually in the form of nitrogen, and human and animal waste entering coastal areas. These excess nutrient loads cause algae to grow, which isn’t a problem in and of itself, Wallace says; but when algae die, they’re consumed by bacteria, whose respiration in turn bumps up CO2 levels in water.
“Unfortunately, this is occurring at the bottom [of the water column], where shellfish organisms live and grow,” Wallace says. Acidification on the East Coast is minutely localized, occurring closest to where nutrients are being released, as well as seasonally; at least one local shellfish farm, on Fishers Island in the Long Island Sound, has contended with its effects.
The second Vycarb pilot, ready to be installed at the East Hampton shellfish hatchery.
Courtesy of Vycarb
Besides CO2, ocean water contains two other forms of dissolved carbon — carbonate (CO3-) and bicarbonate (HCO3) — at all times, at differing levels. At low pH (acidic), CO2 prevails; at medium pH, HCO3 is the dominant form; at higher pH, CO3 dominates. Boudinot’s invention is the first real-time measurement for all three, he says. From the dock at the Navy Yard, his pilot system uses carefully calibrated but low-cost sensors to gauge the water’s pH and its corresponding levels of CO2. When it detects elevated levels of the greenhouse gas, the system mitigates it on the spot. It does this by adding a bicarbonate powder that’s a byproduct of agricultural limestone mining in nearby Pennsylvania. Because the bicarbonate powder is alkaline, it increases the water pH and reduces the acidity. “We drive a chemical reaction to increase the pH to convert greenhouse gas- and acid-causing CO2 into bicarbonate, which is HCO3,” Boudinot says. “And HCO3 is what shellfish and fish and lots of marine life prefers over CO2.”
This de-acidifying “buffering” is something shellfish operations already do to water, usually by adding soda ash (NaHCO3), which is also alkaline. Some hatcheries add soda ash constantly, just in case; some wait till acidification causes significant problems. Generally, for an overly busy shellfish farmer to detect acidification takes time and effort. “We’re out there daily, taking a look at the pH and figuring out how much we need to dose it,” explains John “Barley” Dunne, director of the East Hampton Shellfish Hatchery on Long Island. “If this is an automatic system…that would be much less labor intensive — one less thing to monitor when we have so many other things we need to monitor.”
Across the Sound at the hatchery he runs, Dunne annually produces 30 million hard clams, 6 million oysters, and “if we’re lucky, some years we get a million bay scallops,” he says. These mollusks are destined for restoration projects around the town of East Hampton, where they’ll create habitat, filter water, and protect the coastline from sea level rise and storm surge. So far, Dunne’s hatchery has largely escaped the ill effects of acidification, although his bay scallops are having a finicky year and he’s checking to see if acidification might be part of the problem. But “I think it's important to have these solutions ready-at-hand for when the time comes,” he says. That’s why he’s hosting a second, 70-liter Vycarb pilot starting this summer on a dock adjacent to his East Hampton operation; it will amp up to a 50,000 liter-system in a few months.
If it can buffer water over a large area, absolutely this will benefit natural spawns. -- John “Barley” Dunne.
Boudinot hopes this new pilot will act as a proof of concept for hatcheries up and down the East Coast. The area from Maine to Nova Scotia is experiencing the worst of Atlantic acidification, due in part to increased Arctic meltwater combining with Gulf of St. Lawrence freshwater; that decreases saturation of calcium carbonate, making the water more acidic. Boudinot says his system should work to adjust low pH regardless of the cause or locale. The East Hampton system will eventually test and buffer-as-necessary the water that Dunne pumps from the Sound into 100-gallon land-based tanks where larvae grow for two weeks before being transferred to an in-Sound nursery to plump up.
Dunne says this could have positive effects — not only on his hatchery but on wild shellfish populations, too, reducing at least one stressor their larvae experience (others include increasing water temperatures and decreased oxygen levels). “If it can buffer water over a large area, absolutely this will [benefit] natural spawns,” he says.
No one believes the Vycarb model — even if it proves capable of functioning at much greater scale — is the sole solution to acidification in the ocean. Wallace says new water treatment plants in New York City, which reduce nitrogen released into coastal waters, are an important part of the equation. And “certainly, some green infrastructure would help,” says Boudinot, like restoring coastal and tidal wetlands to help filter nutrient runoff.
In the meantime, Boudinot continues to collect data in advance of amping up his own operations. Still unknown is the effect of releasing huge amounts of alkalinity into the ocean. Boudinot says a pH of 9 or higher can be too harsh for marine life, plus it can also trigger a release of CO2 from the water back into the atmosphere. For a third pilot, on Governor’s Island in New York Harbor, Vycarb will install yet another system from which Boudinot’s team will frequently sample to analyze some of those and other impacts. “Let's really make sure that we know what the results are,” he says. “Let's have data to show, because in this carbon world, things behave very differently out in the real world versus on paper.”