The Algorithm Will See You Now
Artificial intelligence in medicine, still in an early phase, stands to transform how doctors and nurses spend their time.
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."
A company uses AI to fight muscle loss and unhealthy aging
In December 2022, a company called BioAge Labs published findings on a drug that worked to prevent muscular atrophy, or the loss of muscle strength and mass, in older people.
There’s a growing need to slow down the aging process. The world’s population is getting older and, according to one estimate, 80 million Americans will be 65 or older by 2040. As we age, the risk of many chronic diseases goes up, from cancer to heart disease to Alzheimer’s.
BioAge Labs, a company based in California, is using genetic data to help people stay healthy for longer. CEO Kristen Fortney was inspired by the genetics of people who live long lives and resist many age-related diseases. In 2015, she started BioAge to study them and develop drug therapies based on the company’s learnings.
The team works with special biobanks that have been collecting blood samples and health data from individuals for up to 45 years. Using artificial intelligence, BioAge is able to find the distinctive molecular features that distinguish those who have healthy longevity from those who don’t.
In December 2022, BioAge published findings on a drug that worked to prevent muscular atrophy, or the loss of muscle strength and mass, in older people. Much of the research on aging has been in worms and mice, but BioAge is focused on human data, Fortney says. “This boosts our chances of developing drugs that will be safe and effective in human patients.”
How it works
With assistance from AI, BioAge measures more than 100,000 molecules in each blood sample, looking at proteins, RNA and metabolites, or small molecules that are produced through chemical processes. The company uses many techniques to identify these molecules, some of which convert the molecules into charged atoms and then separating them according to their weight and charge. The resulting data is very complex, with many thousands of data points from patients being followed over the decades.
BioAge validates its targets by examining whether a pathway going awry is actually linked to the development of diseases, based on the company’s analysis of biobank health records and blood samples. The team uses AI and machine learning to identify these pathways, and the key proteins in the unhealthy pathways become their main drug targets. “The approach taken by BioAge is an excellent example of how we can harness the power of big data and advances in AI technology to identify new drugs and therapeutic targets,” says Lorna Harries, a professor of molecular genetics at the University of Exeter Medical School.
Martin Borch Jensen is the founder of Gordian Biotechnology, a company focused on using gene therapy to treat aging. He says BioAge’s use of AI allows them to speed up the process of finding promising drug candidates. However, it remains a challenge to separate pathologies from aspects of the natural aging process that aren’t necessarily bad. “Some of the changes are likely protective responses to things going wrong,” Jensen says. “Their data doesn’t…distinguish that so they’ll need to validate and be clever.”
Developing a drug for muscle loss
BioAge decided to focus on muscular atrophy because it affects many elderly people, making it difficult to perform everyday activities and increasing the risk of falls. Using the biobank samples, the team modeled different pathways that looked like they could improve muscle health. They found that people who had faster walking speeds, better grip strength and lived longer had higher levels of a protein called apelin.
Apelin is a peptide, or a small protein, that circulates in the blood. It is involved in the process by which exercise increases and preserves muscle mass. BioAge wondered if they could prevent muscular atrophy by increasing the amount of signaling in the apelin pathway. Instead of the long process of designing a drug, they decided to repurpose an existing drug made by another biotech company. This company, called Amgen, had explored the drug as a way to treat heart failure. It didn’t end up working for that purpose, but BioAge took note that the drug did seem to activate the apelin pathway.
BioAge tested its new, repurposed drug, BGE-105, and, in a phase 1 clinical trial, it protected subjects from getting muscular atrophy compared to a placebo group that didn’t receive the drug. Healthy volunteers over age 65 received infusions of the drug during 10 days spent in bed, as if they were on bed rest while recovering from an illness or injury; the elderly are especially vulnerable to muscle loss in this situation. The 11 people taking BGE-105 showed a 100 percent improvement in thigh circumference compared to 10 people taking the placebo. Ultrasound observations also revealed that the group taking the durg had enhanced muscle quality and a 73 percent increase in muscle thickness. One volunteer taking BGE-105 did have muscle loss compared to the the placebo group.
Heather Whitson, the director of the Duke University Centre for the study of aging and human development, says that, overall, the results are encouraging. “The clinical findings so far support the premise that AI can help us sort through enormous amounts of data and identify the most promising points for beneficial interventions.”
More studies are needed to find out which patients benefit the most and whether there are side effects. “I think further studies will answer more questions,” Whitson says, noting that BGE-105 was designed to enhance only one aspect of physiology associated with exercise, muscle strength. But exercise itself has many other benefits on mood, sleep, bones and glucose metabolism. “We don’t know whether BGE-105 will impact these other outcomes,” she says.
The future
BioAge is planning phase 2 trials for muscular atrophy in patients with obesity and those who have been hospitalized in an intensive care unit. Using the data from biobanks, they’ve also developed another drug, BGE-100, to treat chronic inflammation in the brain, a condition that can worsen with age and contributes to neurodegenerative diseases. The team is currently testing the drug in animals to assess its effects and find the right dose.
BioAge envisions that its drugs will have broader implications for health than treating any one specific disease. “Ultimately, we hope to pioneer a paradigm shift in healthcare, from treatment to prevention, by targeting the root causes of aging itself,” Fortney says. “We foresee a future where healthy longevity is within reach for all.”
How old fishing nets turn into chairs, car mats and Prada bags
A company called Aquafil is turning the fibers from fishing nets and old carpets into new threads for chairs, car mats, bikinis and Prada bags.
Discarded nylon fishing nets in the oceans are among the most harmful forms of plastic pollution. Every year, about 640,000 tons of fishing gear are left in our oceans and other water bodies to turn into death traps for marine life. London-based non-profit World Animal Protection estimates that entanglement in this “ghost gear” kills at least 136,000 seals, sea lions and large whales every year. Experts are challenged to estimate how many birds, turtles, fish and other species meet the same fate because the numbers are so high.
Since 2009, Giulio Bonazzi, the son of a small textile producer in northern Italy, has been working on a solution: an efficient recycling process for nylon. As CEO and chairman of a company called Aquafil, Bonazzi is turning the fibers from fishing nets – and old carpets – into new threads for car mats, Adidas bikinis, environmentally friendly carpets and Prada bags.
For Bonazzi, shifting to recycled nylon was a question of survival for the family business. His parents founded a textile company in 1959 in a garage in Verona, Italy. Fifteen years later, they started Aquafil to produce nylon for making raincoats, an enterprise that led to factories on three continents. But before the turn of the century, cheap products from Asia flooded the market and destroyed Europe’s textile production. When Bonazzi had finished his business studies and prepared to take over the family company, he wondered how he could produce nylon, which is usually produced from petrochemicals, in a way that was both successful and ecologically sustainable.
The question led him on an intellectual journey as he read influential books by activists such as world-renowned marine biologist Sylvia Earle and got to know Michael Braungart, who helped develop the Cradle-to-Cradle ethos of a circular economy. But the challenges of applying these ideologies to his family business were steep. Although fishing nets have become a mainstay of environmental fashion ads—and giants like Dupont and BASF have made breakthroughs in recycling nylon—no one had been able to scale up these efforts.
For ten years, Bonazzi tinkered with ideas for a proprietary recycling process. “It’s incredibly difficult because these products are not made to be recycled,” Bonazzi says. One complication is the variety of materials used in older carpets. “They are made to be beautiful, to last, to be useful. We vastly underestimated the difficulty when we started.”
Soon it became clear to Bonazzi that he needed to change the entire production process. He found a way to disintegrate old fibers with heat and pull new strings from the discarded fishing nets and carpets. In 2022, his company Aquafil produced more than 45,000 tons of Econyl, which is 100% recycled nylon, from discarded waste.
More than half of Aquafil’s recyclate is from used goods. According to the company, the recycling saves 90 percent of the CO2 emissions compared to the production of conventional nylon. That amounts to saving 57,100 tons of CO2 equivalents for every 10,000 tons of Econyl produced.
Bonazzi collects fishing nets from all over the world, including Norway and Chile—which have the world’s largest salmon productions—in addition to the Mediterranean, Turkey, India, Japan, Thailand, the Philippines, Pakistan, and New Zealand. He counts the government leadership of Seychelles as his most recent client; the island has prohibited ships from throwing away their fishing nets, creating the demand for a reliable recycler. With nearly 3,000 employees, Aquafil operates almost 40 collection and production sites in a dozen countries, including four collection sites for old carpets in the U.S., located in California and Arizona.
First, the dirty nets are gathered, washed and dried. Bonazzi explains that nets often have been treated with antifouling agents such as copper oxide. “We recycle the coating separately,” he says via Zoom from his home near Verona. “Copper oxide is a useful substance, why throw it away?”
Still, only a small percentage of Aquafil’s products are made from nets fished out of the ocean, so your new bikini may not have saved a strangled baby dolphin. “Generally, nylon recycling is a good idea,” says Christian Schiller, the CEO of Cirplus, the largest global marketplace for recyclates and plastic waste. “But contrary to what consumers think, people rarely go out to the ocean to collect ghost nets. Most are old, discarded nets collected on land. There’s nothing wrong with this, but I find it a tad misleading to label the final products as made from ‘ocean plastic,’ prompting consumers to think they’re helping to clean the oceans by buying these products.”
Aquafil gets most of its nets from aqua farms. Surprisingly, one of Aquafil’s biggest problems is finding enough waste. “I know, it’s hard to believe because waste is everywhere,” Bonazzi says. “But we need to find it in reliable quantity and quality.” He has invested millions in establishing reliable logistics to source the fishing nets. Then the nets get shredded into granules that can be turned into car mats for the new Hyundai Ioniq 5 or a Gucci swimsuit.
The process works similarly with carpets. In the U.S. alone, 3.5 billion pounds of carpet are discarded in landfills every year, and less than 3 percent are currently recycled. Aquafil has built a recycling plant in Phoenix to help divert 12,500 tons of carpets from the landfill every year. The carpets are shredded and deconstructed into three components: fillers such as calcium carbonate will be reused in the cement industry, synthetic fibers like polypropylene can be used for engineering plastics, and nylon. Only the pelletized nylon gets shipped back to Europe for the production of Econyl. “We ship only what’s necessary,” Bonazzi says. Nearly 50 percent of his nylon in Italy and Slovenia is produced from recyclate, and he hopes to increase the percentage to two-thirds in the next two years.
His clients include Interface, the leading world pioneer for sustainable flooring, and many other carpet producers plus more than 2500 fashion labels, including Gucci, Prada, Patagonia, Louis Vuitton, Adidas and Stella McCartney. “Stella McCartney just introduced a parka that’s made 100 percent from Econyl,” Bonazzi says. “We’re also in a lot of sportswear because Nylon is a good fabric for swimwear and for yoga clothes.” Next, he’s looking into sunglasses and chairs made with Econyl - for instance, the flexible ergonomic noho chair, designed by New Zealand company Formway.
“When I look at a landfill, I see a gold mine," Bonazzi says.
“Bonazzi decided many years ago to invest in the production of recycled nylon though industry giants halted similar plans after losing large investments,” says Anika Herrmann, vice president of the German Greentech-competitor Camm Solutions, which creates bio-based polymers from cane sugar and other ag waste. “We need role models like Bonazzi who create sustainable solutions with courage and a pioneering spirit. Like Aquafil, we count on strategic partnerships to enable fast upscaling along the entire production chain.”
Bonazzi’s recycled nylon is still five to 10 percent more expensive than conventionally produced material. However, brands are increasingly bending to the pressure of eco-conscious consumers who demand sustainable fashion. What helped Bonazzi was the recent rise of oil prices and the pressure on industries to reduce their carbon footprint. Now Bonazzi says, “When I look at a landfill, I see a gold mine.”
Ideally, the manufacturers take the products back when the client is done with it, and because the nylon can theoretically be reused nearly infinitely, the chair or bikini could be made into another chair or bikini. “But honestly,” Bonazzi half-jokes, “if someone returns a McCartney parka to me, I’ll just resell it because it’s so expensive.”
The next step: Bonazzi wants to reshape the entire nylon industry by pivoting from post-consumer nylon to plant-based nylon. In 2017, he began producing “nylon-6,” together with Genomatica in San Diego. The process uses sugar instead of petroleum. “The idea is to make the very same molecule from sugar, not from oil,” he says. The demonstration plant in Ljubljana, Slovenia, has already produced several hundred tons of nylon, and Genomatica is collaborating with Lululemon to produce plant-based yoga wear.
Bonazzi acknowledges that his company needs a few more years before the technology is ready to meet his ultimate goal, producing only recyclable products with no petrochemicals, low emissions and zero waste on an industrial scale. “Recycling is not enough,” he says. “You also need to produce the primary material in a sustainable way, with a low carbon footprint.”