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."
A company uses AI to fight muscle loss and unhealthy aging
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.”
This article is part of the magazine, "The Future of Science In America: The Election Issue," co-published by LeapsMag, the Aspen Institute Science & Society Program, and GOOD.
When COVID-19 cases were surging in New York City in early spring, Chitra Mohan, a postdoctoral fellow at Weill Cornell, was overwhelmed with worry. But the pandemic was only part of her anxieties. Having come to the United States from India on a student visa that allowed her to work for a year after completing her degree, she had applied for a two-year extension, typically granted for those in STEM fields. But due to a clerical error—Mohan used an electronic signatureinstead of a handwritten one— her application was denied and she could no longerwork in the United States.
"I was put on unpaid leave and I lost my apartment and my health insurance—and that was in the middle of COVID!" she says.
Meanwhile her skills were very much needed in those unprecedented times. A molecular biologist studying how DNA can repair itself, Mohan was trained in reverse transcription polymerase chain reaction or RT-PCR—a lab technique that detects pathogens and is used to diagnose COVID-19. Mohan wanted to volunteer at testing centers, but because she couldn't legally work in the U.S., she wasn't allowed to help either. She moved to her cousin's house, hired a lawyer, and tried to restore her work status.
"I spent about $4,000 on lawyer fees and another $1,200 to pay for the motions I filed," she recalls. "I had to borrow money from my parents and my cousin because without my salary I just didn't have the $7,000 at hand." But the already narrow window of opportunity slammed completely shut when the Trump administration suspended issuing new visas for foreign researchers in June. All Mohan's attempts were denied. In August, she had to leave the country. "Given the recent work visa ban by the administration, all my options in the U.S. are closed," she wrote a bitter note on Twitter. "I have to uproot my entire life in NY for the past 6 years and leave." She eventually found a temporary position in Calcutta, where she can continue research.
Mohan is hardly alone in her visa saga. Many foreign scholars on H- and J-type visas and other permits that let them remain employed in America had been struggling to keep their rights to continue research, which in certain cases is crucial to battling the pandemic. Some had to leave the country, some filed every possible extension to buy time, and others are stuck in their home countries, unable to return. The already cumbersome process of applying for visas and extensions became crippled during the lockdowns. But in June, when President Trump extended and expanded immigration restrictions to cut the number of immigrant workers entering the U.S., the new limits left researchers' projects and careers in limbo—and some in jeopardy.
"We have been a beneficiary of this flow of human capacity and resource investment for many generations—and this is now threatened."
Rakesh Ramachandran, whose computational biology work contributed to one of the first coronavirus studies to map out its protein structures—is stranded in India. In early March, he had travelled there to attend a conference and visit the American consulate to stamp his H1 visa for a renewal, already granted. The pandemic shut down both the conference and the consulates, and Ramachandran hasn't been able to come back since. The consulates finally opened in September, but so far the online portal has no available appointment slots. "I'm told to keep trying," Ramachandran says.
The visa restrictions affected researchers worldwide, regardless of disciplines or countries. A Ph.D. student in neuroscience, Morgane Leroux had to do her experiments with mice at Gladstone Institutes in America and analyze the data back home at Sorbonne University in France. She had finished her first round of experiments when the lockdowns forced her to return to Paris, and she hasn't been able to come back to resume her work since. "I can't continue the experiments, which is really frustrating," she says, especially because she doesn't know what it means for her Ph.D. "I may have to entirely change my subject," she says, which she doesn't want to do—it would be a waste of time and money.
But besides wreaking havoc in scholars' personal lives and careers, the visa restrictions had—and will continue to have—tremendous deleterious effects on America's research and its global scientific competitiveness. "It's incredibly short-sighted and self-destructing to restrict the immigration of scientists into the U.S.," says Benjamin G. Neel, who directs the Laura and Isaac Perlmutter Cancer Center at New York University. "If they can't come here, they will go elsewhere," he says, causing a brain drain.
Neel in his lab with postdocs
(Courtesy of Neel)
Neel felt the outcomes of the shortsighted policies firsthand. In the past few months, his lab lost two postdoctoral researchers who had made major strides in understanding the biology of several particularly stubborn, treatment-resistant malignancies. One postdoc studied the underlying mechanisms responsible for 90 percent of pancreatic cancers and half of the colon ones. The other one devised a new system of modeling ovarian cancer in mice to test new therapeutic drug combinations for the deadliest tumor types—but had to return home to China.
"By working around the clock, she was able to get her paper accepted, but she hasn't been able to train us to use this new system, which can set us back six months," Neel says.
Her discoveries also helped the lab secure about $900,000 in grants for new research. Losing people like this is "literally killing the goose that lays the golden eggs," Neel adds. "If you want to make America poor again, this is the way to do it."
Cassidy R. Sugimoto at Indiana University Bloomington, who studies how scientific knowledge is produced and disseminated, says that scientists are the most productive when they are free to move, exchange ideas, and work at labs with the best equipment. Restricting that freedom reduces their achievement.
"Several empirical studied demonstrated the benefits to the U.S. by attracting and retaining foreign scientists. The disproportional number of our Nobel Prize winners were not only foreign-born but also foreign-educated," she says. Scientific advancement bolsters the country's economic prowess, too, so turning scholars away is bad for the economy long-term. "We have been a beneficiary of this flow of human capacity and resource investment for many generations—and this is now threatened," Sugimoto adds—because scientists will look elsewhere. "We are seeing them shifting to other countries that are more hospitable, both ideologically and in terms of health security. Many visiting scholars, postdocs, and graduate students who would otherwise come to the United States are now moving to Canada."
It's not only the Ph.D. students and postdocs who are affected. In some cases, even well-established professors who have already made their marks in the field and direct their own labs at prestigious research institutions may have to pack up and leave the country in the next few months. One scientist who directs a prominent neuroscience lab is betting on his visa renewal and a green card application, but if that's denied, the entire lab may be in jeopardy, as many grants hinge on his ability to stay employed in America.
"It's devastating to even think that it can happen," he says—after years of efforts invested. "I can't even comprehend how it would feel. It would be terrifying and really sad." (He asked to withhold his name for fear that it may adversely affect his applications.) Another scientist who originally shared her story for this article, later changed her mind and withdrew, worrying that speaking out may hurt the entire project, a high-profile COVID-19 effort. It's not how things should work in a democratic country, scientists admit, but that's the reality.
Still, some foreign scholars are speaking up. Mehmet Doğan, a physicist at University of California Berkeley who has been fighting a visa extension battle all year, says it's important to push back in an organized fashion with petitions and engage legislators. "This administration was very creative in finding subtle and not so subtle ways to make our lives more difficult," Doğan says. He adds that the newest rules, proposed by the Department of Homeland Security on September 24, could further limit the time scholars can stay, forcing them into continuous extension battles. That's why the upcoming election might be a turning point for foreign academics. "This election will decide if many of us will see the U.S. as the place to stay and work or whether we look at other countries," Doğan says, echoing the worries of Neel, Sugimoto, and others in academia.
Dogan on Zoom talking to his fellow union members of the Academic Researchers United, a union of almost 5,000 Academic Researchers.
(Credit: Ceyda Durmaz Dogan)
If this year has shown us anything, it is that viruses and pandemics know no borders as they sweep across the globe. Likewise, science can't be restrained by borders either. "Science is an international endeavor," says Neel—and right now humankind now needs unified scientific research more than ever, unhindered by immigration hurdles and visa wars. Humanity's wellbeing in America and beyond depends on it.
[Editor's Note: To read other articles in this special magazine issue, visit the beautifully designed e-reader version.]
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.