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."
The livestock trucks arrived all night. One after the other they backed up to the wood chute leading to a dusty corral and loosed their cargo — 580 head of cattle by the time the last truck pulled away at 3pm the next afternoon. Dan Probert, astride his horse, guided the cows to paddocks of pristine grassland stretching alongside the snow-peaked Wallowa Mountains. They’d spend the summer here grazing bunchgrass and clovers and biscuitroot. The scuffle of their hooves and nibbles of their teeth would mimic the elk, antelope and bison that are thought to have historically roamed this portion of northeastern Oregon’s Zumwalt Prairie, helping grasses grow and restoring health to the soil.
The cows weren’t Probert’s, although the fifth-generation rancher and one other member of the Carman Ranch Direct grass-fed beef collective also raise their own herds here for part of every year. But in spring, when the prairie is in bloom, Probert receives cattle from several other ranchers. As the grasses wither in October, the cows move on to graze fertile pastures throughout the Columbia Basin, which stretches across several Pacific Northwest states; some overwinter on a vegetable farm in central Washington, feeding on corn leaves and pea vines left behind after harvest.
Sharing land and other resources among farmers isn’t new. But research shows it may be increasingly relevant in a time of climatic upheaval, potentially influencing “farmers to adopt environmentally friendly practices and agricultural innovation,” according to a 2021 paper in the Journal of Economic Surveys. Farmers might share knowledge about reducing pesticide use, says Heather Frambach, a supply chain consultant who works with farmers in California and elsewhere. As a group they may better qualify for grants to monitor soil and water quality.
Most research around such practices applies to cooperatives, whose owner-members equally share governance and profits. But a collective like Carman Ranch’s — spearheaded by fourth-generation rancher Cory Carman, who purchases beef from eight other ranchers to sell under one “regeneratively” certified brand — shows when producers band together, they can achieve eco-benefits that would be elusive if they worked alone.
Vitamins and minerals in soil pass into plants through their roots, then into cattle as they graze, then back around as the cows walk around pooping.
Carman knows from experience. Taking over her family's land in 2003, she started selling grass-fed beef “because I really wanted to figure out how to not participate in the feedlot world, to have a healthier product. I didn't know how we were going to survive,” she says. Part of her land sits on a degraded portion of Zumwalt Prairie replete with invasive grasses; working to restore it, she thought, “What good does it do to kill myself trying to make this ranch more functional? If you want to make a difference, change has to be more than single entrepreneurs on single pieces of land. It has to happen at a community level.” The seeds of her collective were sown.
Raising 100 percent grass-fed beef requires land that’s got something for cows to graze in every season — which most collective members can’t access individually. So, they move cattle around their various parcels. It’s practical, but it also restores nutrient flows “to the way they used to move, from lowlands and canyons during the winter to higher-up places as the weather gets hot,” Carman says. Meaning, vitamins and minerals in soil pass into plants through their roots, then into cattle as they graze, then back around as the cows walk around pooping.
Cory Carman sells grass-fed beef, which requires land that’s got something for cows to graze in every season.
Courtesy Cory Carman
Each collective member has individual ecological goals: Carman brought in pigs to root out invasive grasses and help natives flourish. Probert also heads a more conventional grain-finished beef collective with 100 members, and their combined 6.5 million ranchland acres were eligible for a grant supporting climate-friendly practices, which compels them to improve soil and water health and biodiversity and make their product “as environmentally friendly as possible,” Probert says. The Washington veg farmer reduced tilling and pesticide use thanks to the ecoservices of visiting cows. Similarly, a conventional hay farmer near Carman has reduced his reliance on fertilizer by letting cattle graze the cover crops he plants on 80 acres.
Additionally, the collective must meet the regenerative standards promised on their label — another way in which they work together to achieve ecological goals. Says David LeZaks, formerly a senior fellow at finance-focused ecology nonprofit Croatan Institute, it’s hard for individual farmers to access monetary assistance. “But it's easier to get financing flowing when you increase the scale with cooperatives or collectives,” he says. “This supports producers in ways that can lead to better outcomes on the landscape.”
New, smaller scale farmers might gain the most from collective and cooperative models.
For example, it can help them minimize waste by using more of an animal, something our frugal ancestors excelled at. Small-scale beef producers normally throw out hides; Thousand Hills’ 50 regenerative beef producers together have enough to sell to Timberland to make carbon-neutral leather. In another example, working collectively resulted in the support of more diverse farms: Meadowlark Community Mill in Wisconsin went from working with one wheat grower, to sourcing from several organic wheat growers marketing flour under one premium brand.
Another example shows how these collaborations can foster greater equity, among other benefits: The Federation of Southern Cooperatives has a mission to support Black farmers as they build community health. It owns several hundred forest acres in Alabama, where it teaches members to steward their own forest land and use it to grow food — one member coop raises goats to graze forest debris and produce milk. Adding the combined acres of member forest land to the Federation’s, the group qualified for a federal conservation grant that will keep this resource available for food production, and community environmental and mental health benefits. “That's the value-add of the collective land-owner structure,” says Dãnia Davy, director of land retention and advocacy.
New, smaller scale farmers might gain the most from collective and cooperative models, says Jordan Treakle, national program coordinator of the National Family Farm Coalition (NFFC). Many of them enter farming specifically to raise healthy food in healthy ways — with organic production, or livestock for soil fertility. With land, equipment and labor prohibitively expensive, farming collectively allows shared costs and risk that buy farmers the time necessary to “build soil fertility and become competitive” in the marketplace, Treakle says. Just keeping them in business is an eco-win; when small farms fail, they tend to get sold for development or absorbed into less-diversified operations, so the effects of their success can “reverberate through the entire local economy.”
Frambach, the supply chain consultant, has been experimenting with what she calls “collaborative crop planning,” where she helps farmers strategize what they’ll plant as a group. “A lot of them grow based on what they hear their neighbor is going to do, and that causes really poor outcomes,” she says. “Nobody replanted cauliflower after the [atmospheric rivers in California] this year and now there's a huge shortage of cauliflower.” A group plan can avoid the under-planting that causes farmers to lose out on revenue.
It helps avoid overplanted crops, too, which small farmers might have to plow under or compost. Larger farmers, conversely, can sell surplus produce into the upcycling market — to Matriark Foods, for example, which turns it into value-add products like pasta sauce for companies like Sysco that supply institutional kitchens at colleges and hospitals. Frambach and Anna Hammond, Matriark’s CEO, want to collectivize smaller farmers so that they can sell to the likes of Matriark and “not lose an incredible amount of income,” Hammond says.
Ultimately, farming is fraught with challenges and even collectivizing doesn’t guarantee that farms will stay in business. But with agriculture accounting for almost 30 percent of greenhouse gas emissions globally, there's an “urgent” need to shift farming practices to more environmentally sustainable models, as well as a “demand in the marketplace for it,” says NFFC’s Treakle. “The growth of cooperative and collective farming can be a huge, huge boon for the ecological integrity of the system.”
Story by Big Think
We live in strange times, when the technology we depend on the most is also that which we fear the most. We celebrate cutting-edge achievements even as we recoil in fear at how they could be used to hurt us. From genetic engineering and AI to nuclear technology and nanobots, the list of awe-inspiring, fast-developing technologies is long.
However, this fear of the machine is not as new as it may seem. Technology has a longstanding alliance with power and the state. The dark side of human history can be told as a series of wars whose victors are often those with the most advanced technology. (There are exceptions, of course.) Science, and its technological offspring, follows the money.
This fear of the machine seems to be misplaced. The machine has no intent: only its maker does. The fear of the machine is, in essence, the fear we have of each other — of what we are capable of doing to one another.
How AI changes things
Sure, you would reply, but AI changes everything. With artificial intelligence, the machine itself will develop some sort of autonomy, however ill-defined. It will have a will of its own. And this will, if it reflects anything that seems human, will not be benevolent. With AI, the claim goes, the machine will somehow know what it must do to get rid of us. It will threaten us as a species.
Well, this fear is also not new. Mary Shelley wrote Frankenstein in 1818 to warn us of what science could do if it served the wrong calling. In the case of her novel, Dr. Frankenstein’s call was to win the battle against death — to reverse the course of nature. Granted, any cure of an illness interferes with the normal workings of nature, yet we are justly proud of having developed cures for our ailments, prolonging life and increasing its quality. Science can achieve nothing more noble. What messes things up is when the pursuit of good is confused with that of power. In this distorted scale, the more powerful the better. The ultimate goal is to be as powerful as gods — masters of time, of life and death.
Should countries create a World Mind Organization that controls the technologies that develop AI?
Back to AI, there is no doubt the technology will help us tremendously. We will have better medical diagnostics, better traffic control, better bridge designs, and better pedagogical animations to teach in the classroom and virtually. But we will also have better winnings in the stock market, better war strategies, and better soldiers and remote ways of killing. This grants real power to those who control the best technologies. It increases the take of the winners of wars — those fought with weapons, and those fought with money.
A story as old as civilization
The question is how to move forward. This is where things get interesting and complicated. We hear over and over again that there is an urgent need for safeguards, for controls and legislation to deal with the AI revolution. Great. But if these machines are essentially functioning in a semi-black box of self-teaching neural nets, how exactly are we going to make safeguards that are sure to remain effective? How are we to ensure that the AI, with its unlimited ability to gather data, will not come up with new ways to bypass our safeguards, the same way that people break into safes?
The second question is that of global control. As I wrote before, overseeing new technology is complex. Should countries create a World Mind Organization that controls the technologies that develop AI? If so, how do we organize this planet-wide governing board? Who should be a part of its governing structure? What mechanisms will ensure that governments and private companies do not secretly break the rules, especially when to do so would put the most advanced weapons in the hands of the rule breakers? They will need those, after all, if other actors break the rules as well.
As before, the countries with the best scientists and engineers will have a great advantage. A new international détente will emerge in the molds of the nuclear détente of the Cold War. Again, we will fear destructive technology falling into the wrong hands. This can happen easily. AI machines will not need to be built at an industrial scale, as nuclear capabilities were, and AI-based terrorism will be a force to reckon with.
So here we are, afraid of our own technology all over again.
What is missing from this picture? It continues to illustrate the same destructive pattern of greed and power that has defined so much of our civilization. The failure it shows is moral, and only we can change it. We define civilization by the accumulation of wealth, and this worldview is killing us. The project of civilization we invented has become self-cannibalizing. As long as we do not see this, and we keep on following the same route we have trodden for the past 10,000 years, it will be very hard to legislate the technology to come and to ensure such legislation is followed. Unless, of course, AI helps us become better humans, perhaps by teaching us how stupid we have been for so long. This sounds far-fetched, given who this AI will be serving. But one can always hope.