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."
Scientists turn pee into power in Uganda
At the edge of a dirt road flanked by trees and green mountains outside the town of Kisoro, Uganda, sits the concrete building that houses Sesame Girls School, where girls aged 11 to 19 can live, learn and, at least for a while, safely use a toilet. In many developing regions, toileting at night is especially dangerous for children. Without electrical power for lighting, kids may fall into the deep pits of the latrines through broken or unsteady floorboards. Girls are sometimes assaulted by men who hide in the dark.
For the Sesame School girls, though, bright LED lights, connected to tiny gadgets, chased the fears away. They got to use new, clean toilets lit by the power of their own pee. Some girls even used the light provided by the latrines to study.
Urine, whether animal or human, is more than waste. It’s a cheap and abundant resource. Each day across the globe, 8.1 billion humans make 4 billion gallons of pee. Cows, pigs, deer, elephants and other animals add more. By spending money to get rid of it, we waste a renewable resource that can serve more than one purpose. Microorganisms that feed on nutrients in urine can be used in a microbial fuel cell that generates electricity – or "pee power," as the Sesame girls called it.
Plus, urine contains water, phosphorus, potassium and nitrogen, the key ingredients plants need to grow and survive. Human urine could replace about 25 percent of current nitrogen and phosphorous fertilizers worldwide and could save water for gardens and crops. The average U.S. resident flushes a toilet bowl containing only pee and paper about six to seven times a day, which adds up to about 3,500 gallons of water down per year. Plus cows in the U.S. produce 231 gallons of the stuff each year.
Pee power
A conventional fuel cell uses chemical reactions to produce energy, as electrons move from one electrode to another to power a lightbulb or phone. Ioannis Ieropoulos, a professor and chair of Environmental Engineering at the University of Southampton in England, realized the same type of reaction could be used to make a fuel from microbes in pee.
Bacterial species like Shewanella oneidensis and Pseudomonas aeruginosa can consume carbon and other nutrients in urine and pop out electrons as a result of their digestion. In a microbial fuel cell, one electrode is covered in microbes, immersed in urine and kept away from oxygen. Another electrode is in contact with oxygen. When the microbes feed on nutrients, they produce the electrons that flow through the circuit from one electrod to another to combine with oxygen on the other side. As long as the microbes have fresh pee to chomp on, electrons keep flowing. And after the microbes are done with the pee, it can be used as fertilizer.
These microbes are easily found in wastewater treatment plants, ponds, lakes, rivers or soil. Keeping them alive is the easy part, says Ieropoulos. Once the cells start producing stable power, his group sequences the microbes and keeps using them.
Like many promising technologies, scaling these devices for mass consumption won’t be easy, says Kevin Orner, a civil engineering professor at West Virginia University. But it’s moving in the right direction. Ieropoulos’s device has shrunk from the size of about three packs of cards to a large glue stick. It looks and works much like a AAA battery and produce about the same power. By itself, the device can barely power a light bulb, but when stacked together, they can do much more—just like photovoltaic cells in solar panels. His lab has produced 1760 fuel cells stacked together, and with manufacturing support, there’s no theoretical ceiling, he says.
Although pure urine produces the most power, Ieropoulos’s devices also work with the mixed liquids of the wastewater treatment plants, so they can be retrofit into urban wastewater utilities.
This image shows how the pee-powered system works. Pee feeds bacteria in the stack of fuel cells (1), which give off electrons (2) stored in parallel cylindrical cells (3). These cells are connected to a voltage regulator (4), which smooths out the electrical signal to ensure consistent power to the LED strips lighting the toilet.
Courtesy Ioannis Ieropoulos
Key to the long-term success of any urine reclamation effort, says Orner, is avoiding what he calls “parachute engineering”—when well-meaning scientists solve a problem with novel tech and then abandon it. “The way around that is to have either the need come from the community or to have an organization in a community that is committed to seeing a project operate and maintained,” he says.
Success with urine reclamation also depends on the economy. “If energy prices are low, it may not make sense to recover energy,” says Orner. “But right now, fertilizer prices worldwide are generally pretty high, so it may make sense to recover fertilizer and nutrients.” There are obstacles, too, such as few incentives for builders to incorporate urine recycling into new construction. And any hiccups like leaks or waste seepage will cost builders money and reputation. Right now, Orner says, the risks are just too high.
Despite the challenges, Ieropoulos envisions a future in which urine is passed through microbial fuel cells at wastewater treatment plants, retrofitted septic tanks, and building basements, and is then delivered to businesses to use as agricultural fertilizers. Although pure urine produces the most power, Ieropoulos’s devices also work with the mixed liquids of the wastewater treatment plants, so they can be retrofitted into urban wastewater utilities where they can make electricity from the effluent. And unlike solar cells, which are a common target of theft in some areas, nobody wants to steal a bunch of pee.
When Ieropoulos’s team returned to wrap up their pilot project 18 months later, the school’s director begged them to leave the fuel cells in place—because they made a major difference in students’ lives. “We replaced it with a substantial photovoltaic panel,” says Ieropoulos, They couldn’t leave the units forever, he explained, because of intellectual property reasons—their funders worried about theft of both the technology and the idea. But the photovoltaic replacement could be stolen, too, leaving the girls in the dark.
The story repeated itself at another school, in Nairobi, Kenya, as well as in an informal settlement in Durban, South Africa. Each time, Ieropoulos vowed to return. Though the pandemic has delayed his promise, he is resolute about continuing his work—it is a moral and legal obligation. “We've made a commitment to ourselves and to the pupils,” he says. “That's why we need to go back.”
Urine as fertilizer
Modern day industrial systems perpetuate the broken cycle of nutrients. When plants grow, they use up nutrients the soil. We eat the plans and excrete some of the nutrients we pass them into rivers and oceans. As a result, farmers must keep fertilizing the fields while our waste keeps fertilizing the waterways, where the algae, overfertilized with nitrogen, phosphorous and other nutrients grows out of control, sucking up oxygen that other marine species need to live. Few global communities remain untouched by the related challenges this broken chain create: insufficient clean water, food, and energy, and too much human and animal waste.
The Rich Earth Institute in Vermont runs a community-wide urine nutrient recovery program, which collects urine from homes and businesses, transports it for processing, and then supplies it as fertilizer to local farms.
One solution to this broken cycle is reclaiming urine and returning it back to the land. The Rich Earth Institute in Vermont is one of several organizations around the world working to divert and save urine for agricultural use. “The urine produced by an adult in one day contains enough fertilizer to grow all the wheat in one loaf of bread,” states their website.
Notably, while urine is not entirely sterile, it tends to harbor fewer pathogens than feces. That’s largely because urine has less organic matter and therefore less food for pathogens to feed on, but also because the urinary tract and the bladder have built-in antimicrobial defenses that kill many germs. In fact, the Rich Earth Institute says it’s safe to put your own urine onto crops grown for home consumption. Nonetheless, you’ll want to dilute it first because pee usually has too much nitrogen and can cause “fertilizer burn” if applied straight without dilution. Other projects to turn urine into fertilizer are in progress in Niger, South Africa, Kenya, Ethiopia, Sweden, Switzerland, The Netherlands, Australia, and France.
Eleven years ago, the Institute started a program that collects urine from homes and businesses, transports it for processing, and then supplies it as fertilizer to local farms. By 2021, the program included 180 donors producing over 12,000 gallons of urine each year. This urine is helping to fertilize hay fields at four partnering farms. Orner, the West Virginia professor, sees it as a success story. “They've shown how you can do this right--implementing it at a community level scale."
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five, featuring interviews with Dr. David Spiegel, associate chair of psychiatry and behavioral sciences at Stanford, and Dr. Filip Swirski, professor of medicine and cardiology at the Icahn School of Medicine at Mount Sinai.
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Here are the promising studies covered in this week's Friday Five, featuring interviews with Dr. David Spiegel, associate chair of psychiatry and behavioral sciences at Stanford, and Dr. Filip Swirski, professor of medicine and cardiology at the Icahn School of Medicine at Mount Sinai.
- Breathing this way cuts down on anxiety*
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* This video with Dr. Andrew Huberman of Stanford shows exactly how to do the breathing practice.
This podcast originally aired on March 3, 2023.