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."
New Podcast: George Church on Woolly Mammoths, Organ Transplants, and Covid Vaccines
Dr. George Church, a leading pioneer of gene editing, updates our listeners on several of his noteworthy projects.
The "Making Sense of Science" podcast features interviews with leading medical and scientific experts about the latest developments and the big ethical and societal questions they raise. This monthly podcast is hosted by journalist Kira Peikoff, founding editor of the award-winning science outlet Leaps.org.
This month, our guest is notable genetics pioneer Dr. George Church of Harvard Medical School. Dr. Church has remarkably bold visions for how innovation in science can fundamentally transform the future of humanity and our planet. His current moonshot projects include: de-extincting some of the woolly mammoth's genes to create a hybrid Asian elephant with the cold-tolerance traits of the woolly mammoth, so that this animal can re-populate the Arctic and help stave off climate change; reversing chronic diseases of aging through gene therapy, which he and colleagues are now testing in dogs; and transplanting genetically engineered pig organs to humans to eliminate the tragically long waiting lists for organs. Hear Dr. Church discuss all this and more on our latest episode.
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Kira Peikoff was the editor-in-chief of Leaps.org from 2017 to 2021. As a journalist, her work has appeared in The New York Times, Newsweek, Nautilus, Popular Mechanics, The New York Academy of Sciences, and other outlets. She is also the author of four suspense novels that explore controversial issues arising from scientific innovation: Living Proof, No Time to Die, Die Again Tomorrow, and Mother Knows Best. Peikoff holds a B.A. in Journalism from New York University and an M.S. in Bioethics from Columbia University. She lives in New Jersey with her husband and two young sons. Follow her on Twitter @KiraPeikoff.
Beyond Henrietta Lacks: How the Law Has Denied Every American Ownership Rights to Their Own Cells
A 2017 portrait of Henrietta Lacks.
The common perception is that Henrietta Lacks was a victim of poverty and racism when in 1951 doctors took samples of her cervical cancer without her knowledge or permission and turned them into the world's first immortalized cell line, which they called HeLa. The cell line became a workhorse of biomedical research and facilitated the creation of medical treatments and cures worth untold billions of dollars. Neither Lacks nor her family ever received a penny of those riches.
But racism and poverty is not to blame for Lacks' exploitation—the reality is even worse. In fact all patients, then and now, regardless of social or economic status, have absolutely no right to cells that are taken from their bodies. Some have called this biological slavery.
How We Got Here
The case that established this legal precedent is Moore v. Regents of the University of California.
John Moore was diagnosed with hairy-cell leukemia in 1976 and his spleen was removed as part of standard treatment at the UCLA Medical Center. On initial examination his physician, David W. Golde, had discovered some unusual qualities to Moore's cells and made plans prior to the surgery to have the tissue saved for research rather than discarded as waste. That research began almost immediately.
"On both sides of the case, legal experts and cultural observers cautioned that ownership of a human body was the first step on the slippery slope to 'bioslavery.'"
Even after Moore moved to Seattle, Golde kept bringing him back to Los Angeles to collect additional samples of blood and tissue, saying it was part of his treatment. When Moore asked if the work could be done in Seattle, he was told no. Golde's charade even went so far as claiming to find a low-income subsidy to pay for Moore's flights and put him up in a ritzy hotel to get him to return to Los Angeles, while paying for those out of his own pocket.
Moore became suspicious when he was asked to sign new consent forms giving up all rights to his biological samples and he hired an attorney to look into the matter. It turned out that Golde had been lying to his patient all along; he had been collecting samples unnecessary to Moore's treatment and had turned them into a cell line that he and UCLA had patented and already collected millions of dollars in compensation. The market for the cell lines was estimated at $3 billion by 1990.
Moore felt he had been taken advantage of and filed suit to claim a share of the money that had been made off of his body. "On both sides of the case, legal experts and cultural observers cautioned that ownership of a human body was the first step on the slippery slope to 'bioslavery,'" wrote Priscilla Wald, a professor at Duke University whose career has focused on issues of medicine and culture. "Moore could be viewed as asking to commodify his own body part or be seen as the victim of the theft of his most private and inalienable information."
The case bounced around different levels of the court system with conflicting verdicts for nearly six years until the California Supreme Court ruled on July 9, 1990 that Moore had no legal rights to cells and tissue once they were removed from his body.
The court made a utilitarian argument that the cells had no value until scientists manipulated them in the lab. And it would be too burdensome for researchers to track individual donations and subsequent cell lines to assure that they had been ethically gathered and used. It would impinge on the free sharing of materials between scientists, slow research, and harm the public good that arose from such research.
"In effect, what Moore is asking us to do is impose a tort duty on scientists to investigate the consensual pedigree of each human cell sample used in research," the majority wrote. In other words, researchers don't need to ask any questions about the materials they are using.
One member of the court did not see it that way. In his dissent, Stanley Mosk raised the specter of slavery that "arises wherever scientists or industrialists claim, as defendants have here, the right to appropriate and exploit a patient's tissue for their sole economic benefit—the right, in other words, to freely mine or harvest valuable physical properties of the patient's body. … This is particularly true when, as here, the parties are not in equal bargaining positions."
Mosk also cited the appeals court decision that the majority overturned: "If this science has become for profit, then we fail to see any justification for excluding the patient from participation in those profits."
But the majority bought the arguments that Golde, UCLA, and the nascent biotechnology industry in California had made in amici briefs filed throughout the legal proceedings. The road was now cleared for them to develop products worth billions without having to worry about or share with the persons who provided the raw materials upon which their research was based.
Critical Views
Biomedical research requires a continuous and ever-growing supply of human materials for the foundation of its ongoing work. If an increasing number of patients come to feel as John Moore did, that the system is ripping them off, then they become much less likely to consent to use of their materials in future research.
Some legal and ethical scholars say that donors should be able to limit the types of research allowed for their tissues and researchers should be monitored to assure compliance with those agreements. For example, today it is commonplace for companies to certify that their clothing is not made by child labor, their coffee is grown under fair trade conditions, that food labeled kosher is properly handled. Should we ask any less of our pharmaceuticals than that the donors whose cells made such products possible have been treated honestly and fairly, and share in the financial bounty that comes from such drugs?
Protection of individual rights is a hallmark of the American legal system, says Lisa Ikemoto, a law professor at the University of California Davis. "Putting the needs of a generalized public over the interests of a few often rests on devaluation of the humanity of the few," she writes in a reimagined version of the Moore decision that upholds Moore's property claims to his excised cells. The commentary is in a chapter of a forthcoming book in the Feminist Judgment series, where authors may only use legal precedent in effect at the time of the original decision.
"Why is the law willing to confer property rights upon some while denying the same rights to others?" asks Radhika Rao, a professor at the University of California, Hastings College of the Law. "The researchers who invest intellectual capital and the companies and universities that invest financial capital are permitted to reap profits from human research, so why not those who provide the human capital in the form of their own bodies?" It might be seen as a kind of sweat equity where cash strapped patients make a valuable in kind contribution to the enterprise.
The Moore court also made a big deal about inhibiting the free exchange of samples between scientists. That has become much less the situation over the more than three decades since the decision was handed down. Ironically, this decision, as well as other laws and regulations, have since strengthened the power of patents in biomedicine and by doing so have increased secrecy and limited sharing.
"Although the research community theoretically endorses the sharing of research, in reality sharing is commonly compromised by the aggressive pursuit and defense of patents and by the use of licensing fees that hinder collaboration and development," Robert D. Truog, Harvard Medical School ethicist and colleagues wrote in 2012 in the journal Science. "We believe that measures are required to ensure that patients not bear all of the altruistic burden of promoting medical research."
Additionally, the increased complexity of research and the need for exacting standardization of materials has given rise to an industry that supplies certified chemical reagents, cell lines, and whole animals bred to have specific genetic traits to meet research needs. This has been more efficient for research and has helped to ensure that results from one lab can be reproduced in another.
The Court's rationale of fostering collaboration and free exchange of materials between researchers also has been undercut by the changing structure of that research. Big pharma has shrunk the size of its own research labs and over the last decade has worked out cooperative agreements with major research universities where the companies contribute to the research budget and in return have first dibs on any findings (and sometimes a share of patent rights) that come out of those university labs. It has had a chilling effect on the exchange of materials between universities.
Perhaps tracking cell line donors and use restrictions on those donations might have been burdensome to researchers when Moore was being litigated. Some labs probably still kept their cell line records on 3x5 index cards, computers were primarily expensive room-size behemoths with limited capacity, the internet barely existed, and there was no cloud storage.
But that was the dawn of a new technological age and standards have changed. Now cell lines are kept in state-of-the-art sub zero storage units, tagged with the source, type of tissue, date gathered and often other information. Adding a few more data fields and contacting the donor if and when appropriate does not seem likely to disrupt the research process, as the court asserted.
Forging the Future
"U.S. universities are awarded almost 3,000 patents each year. They earn more than $2 billion each year from patent royalties. Sharing a modest portion of these profits is a novel method for creating a greater sense of fairness in research relationships that we think is worth exploring," wrote Mark Yarborough, a bioethicist at the University of California Davis Medical School, and colleagues. That was penned nearly a decade ago and those numbers have only grown.
The Michigan BioTrust for Health might serve as a useful model in tackling some of these issues. Dried blood spots have been collected from all newborns for half a century to be tested for certain genetic diseases, but controversy arose when the huge archive of dried spots was used for other research projects. As a result, the state created a nonprofit organization to in essence become a biobank and manage access to these spots only for specific purposes, and also to share any revenue that might arise from that research.
"If there can be no property in a whole living person, does it stand to reason that there can be no property in any part of a living person? If there were, can it be said that this could equate to some sort of 'biological slavery'?" Irish ethicist Asim A. Sheikh wrote several years ago. "Any amount of effort spent pondering the issue of 'ownership' in human biological materials with existing law leaves more questions than answers."
Perhaps the biggest question will arise when -- not if but when -- it becomes possible to clone a human being. Would a human clone be a legal person or the property of those who created it? Current legal precedent points to it being the latter.
Today, October 4, is the 70th anniversary of Henrietta Lacks' death from cancer. Over those decades her immortalized cells have helped make possible miraculous advances in medicine and have had a role in generating billions of dollars in profits. Surviving family members have spoken many times about seeking a share of those profits in the name of social justice; they intend to file lawsuits today. Such cases will succeed or fail on their own merits. But regardless of their specific outcomes, one can hope that they spark a larger public discussion of the role of patients in the biomedical research enterprise and lead to establishing a legal and financial claim for their contributions toward the next generation of biomedical research.