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."
Smartwatches can track COVID-19 symptoms, study finds
University of Michigan researchers found that new signals embedded in heart rate indicated when individuals were infected with COVID-19 and how ill they became.
If a COVID-19 infection develops, a wearable device may eventually be able to clue you in. A study at the University of Michigan found that a smartwatch can monitor how symptoms progress.
The study evaluated the effects of COVID-19 with various factors derived from heart-rate data. This method also could be employed to detect other diseases, such as influenza and the common cold, at home or when medical resources are limited, such as during a pandemic or in developing countries.
Tracking students and medical interns across the country, the University of Michigan researchers found that new signals embedded in heart rate indicated when individuals were infected with COVID-19 and how ill they became.
For instance, they discovered that individuals with COVID-19 experienced an increase in heart rate per step after the onset of their symptoms. Meanwhile, people who reported a cough as one of their COVID-19 symptoms had a much more elevated heart rate per step than those without a cough.
“We previously developed a variety of algorithms to analyze data from wearable devices. So, when the COVID-19 pandemic hit, it was only natural to apply some of these algorithms to see if we can get a better understanding of disease progression,” says Caleb Mayer, a doctoral student in mathematics at the University of Michigan and a co-first author of the study.
People may not internally sense COVID-19’s direct impact on the heart, but “heart rate is a vital sign that gives a picture of overall health," says Daniel Forger, a University of Michigan professor.
Millions of people are tracking their heart rate through wearable devices. This information is already generating a tremendous amount of data for researchers to analyze, says co-author Daniel Forger, professor of mathematics and research professor of computational medicine and bioinformatics at the University of Michigan.
“Heart rate is affected by many different physiological signals,” Forger explains. “For instance, if your lungs aren’t functioning properly, your heart may need to beat faster to meet metabolic demands. Your heart rate has a natural daily rhythm governed by internal biological clocks.” While people may not internally sense COVID-19’s direct impact on the heart, he adds that “heart rate is a vital sign that gives a picture of overall health.”
Among the total of 2,164 participants who enrolled in the student study, 72 undergraduate and graduate students contracted COVID-19, providing wearable data from 50 days before symptom onset to 14 days after. The researchers also analyzed this type of data for 43 medical interns from the Intern Health Study by the Michigan Neuroscience Institute and 29 individuals (who are not affiliated with the university) from the publicly available dataset.
Participants could wear the device on either wrist. They also documented their COVID-19 symptoms, such as fever, shortness of breath, cough, runny nose, vomiting, diarrhea, body aches, loss of taste, loss of smell, and sore throat.
Experts not involved in the study found the research to be productive. “This work is pioneering and reveals exciting new insights into the many important ways that we can derive clinically significant information about disease progression from consumer-grade wearable devices,” says Lisa A. Marsch, director of the Center for Technology and Behavioral Health and a professor in the Geisel School of Medicine at Dartmouth College. “Heart-rate data are among the highest-quality data that can be obtained via wearables.”
Beyond the heart, she adds, “Wearable devices are providing novel insights into individuals’ physiology and behavior in many health domains.” In particular, “this study beautifully illustrates how digital-health methodologies can markedly enhance our understanding of differences in individuals’ experience with disease and health.”
Previous studies had demonstrated that COVID-19 affects cardiovascular functions. Capitalizing on this knowledge, the University of Michigan endeavor took “a giant step forward,” says Gisele Oda, a researcher at the Institute of Biosciences at the University of Sao Paulo in Brazil and an expert in chronobiology—the science of biological rhythms. She commends the researchers for developing a complex algorithm that “could extract useful information beyond the established knowledge that heart rate increases and becomes more irregular in COVID patients.”
Wearable devices open the possibility of obtaining large-scale, long, continuous, and real-time heart-rate data on people performing everyday activities or while sleeping. “Importantly, the conceptual basis of this algorithm put circadian rhythms at the center stage,” Oda says, referring to the physical, mental, and behavioral changes that follow a 24-hour cycle. “What we knew before was often based on short-time heart rate measured at any time of day,” she adds, while noting that heart rate varies between day and night and also changes with activity.
However, without comparison to a control group of people having the common flu, it is difficult to determine if the heart-rate signals are unique to COVID-19 or also occur with other illnesses, says John Torous, an assistant professor of psychiatry at Harvard Medical School who has researched wearable devices. In addition, he points to recent data showing that many wearables, which work by beaming light through the skin, may be less accurate in people with darker skin due to variations in light absorption.
While the results sound interesting, they lack the level of conclusive evidence that would be needed to transform how physicians care for patients. “But it is a good step in learning more about what these wearables can tell us,” says Torous, who is also director of digital psychiatry at Beth Israel Deaconess Medical Center, a Harvard affiliate, in Boston. A follow-up step would entail replicating the results in a different pool of people to “help us realize the full value of this work.”
It is important to note that this research was conducted in university settings during the early phases of the pandemic, with remote learning in full swing amid strict isolation and quarantine mandates in effect. The findings demonstrate that physiological monitoring can be performed using consumer-grade wearable sensors, allowing research to continue without in-person contact, says Sung Won Choi, a professor of pediatrics at the University of Michigan who is principal investigator of the student study.
“The worldwide COVID-19 pandemic interrupted a lot of activities that relied on face-to-face interactions, including clinical research,” Choi says. “Mobile technology proved to be tremendously beneficial during that time, because it allowed us to collect detailed physiological data from research participants remotely over an entire semester.” In fact, the researchers did not have any in-person contact with the students involved in the study. “Everything was done virtually," Choi explains. "Importantly, their willingness to participate in research and share data during this historical time, combined with the capacity of secure cloud storage and novel mathematical analytics, enabled our research teams to identify unique patterns in heart-rate data associated with COVID-19.”
Trading syphilis for malaria: How doctors treated one deadly disease by infecting patients with another
In the 1920s, doctors induced a high fever in patients - so called "fever therapy" - as a way to help them recover from syphilis, though it involved ethical problems.
If you had lived one hundred years ago, syphilis – a bacterial infection spread by sexual contact – would likely have been one of your worst nightmares. Even though syphilis still exists, it can now be detected early and cured quickly with a course of antibiotics. Back then, however, before antibiotics and without an easy way to detect the disease, syphilis was very often a death sentence.
To understand how feared syphilis once was, it’s important to understand exactly what it does if it’s allowed to progress: the infections start off as small, painless sores or even a single sore near the vagina, penis, anus, or mouth. The sores disappear around three to six weeks after the initial infection – but untreated, syphilis moves into a secondary stage, often presenting as a mild rash in various areas of the body (such as the palms of a person’s hands) or through other minor symptoms. The disease progresses from there, often quietly and without noticeable symptoms, sometimes for decades before it reaches its final stages, where it can cause blindness, organ damage, and even dementia. Research indicates, in fact, that as much as 10 percent of psychiatric admissions in the early 20th century were due to dementia caused by syphilis, also known as neurosyphilis.
Like any bacterial disease, syphilis can affect kids, too. Though it’s spread primarily through sexual contact, it can also be transmitted from mother to child during birth, causing lifelong disability.
The poet-physician Aldabert Bettman, who wrote fictionalized poems based on his experiences as a doctor in the 1930s, described the effect syphilis could have on an infant in his poem Daniel Healy:
I always got away clean
when I went out
With the boys.
The night before
I was married
I went out,—But was not so fortunate;
And I infected
My bride.
When little Daniel
Was born
His eyes discharged;
And I dared not tell
That because
I had seen too much
Little Daniel sees not at all
Given the horrors of untreated syphilis, it’s maybe not surprising that people would go to extremes to try and treat it. One of the earliest remedies for syphilis, dating back to 15th century Naples, was using mercury – either rubbing it on the skin where blisters appeared, or breathing it in as a vapor. (Not surprisingly, many people who underwent this type of “treatment” died of mercury poisoning.)
Other primitive treatments included using tinctures made of a flowering plant called guaiacum, as well as inducing “sweat baths” to eliminate the syphilitic toxins. In 1910, an arsenic-based drug called Salvarsan hit the market and was hailed as a “magic bullet” for its ability to target and destroy the syphilis-causing bacteria without harming the patient. However, while Salvarsan was effective in treating early-stage syphilis, it was largely ineffective by the time the infection progressed beyond the second stage. Tens of thousands of people each year continued to die of syphilis or were otherwise shipped off to psychiatric wards due to neurosyphilis.
It was in one of these psychiatric units in the early 20th century that Dr. Julius Wagner-Juaregg got the idea for a potential cure.
Wagner-Juaregg was an Austrian-born physician trained in “experimental pathology” at the University of Vienna. Wagner-Juaregg started his medical career conducting lab experiments on animals and then moved on to work at different psychiatric clinics in Vienna, despite having no training in psychiatry or neurology.
Wagner-Juaregg’s work was controversial to say the least. At the time, medicine – particularly psychiatric medicine – did not have anywhere near the same rigorous ethical standards that doctors, researchers, and other scientists are bound to today. Wagner-Juaregg would devise wild theories about the cause of their psychiatric ailments and then perform experimental procedures in an attempt to cure them. (As just one example, Wagner-Juaregg would sterilize his adolescent male patients, thinking “excessive masturbation” was the cause of their schizophrenia.)
But sometimes these wild theories paid off. In 1883, during his residency, Wagner-Juaregg noted that a female patient with mental illness who had contracted a skin infection and suffered a high fever experienced a sudden (and seemingly miraculous) remission from her psychosis symptoms after the fever had cleared. Wagner-Juaregg theorized that inducing a high fever in his patients with neurosyphilis could help them recover as well.
Eventually, Wagner-Juaregg was able to put his theory to the test. Around 1890, Wagner-Juaregg got his hands on something called tuberculin, a therapeutic treatment created by the German microbiologist Robert Koch in order to cure tuberculosis. Tuberculin would later turn out to be completely ineffective for treating tuberculosis, often creating severe immune responses in patients – but for a short time, Wagner-Juaregg had some success in using tuberculin to help his dementia patients. Giving his patients tuberculin resulted in a high fever – and after completing the treatment, Wagner-Jauregg reported that his patient’s dementia was completely halted. The success was short-lived, however: Wagner-Juaregg eventually had to discontinue tuberculin as a treatment, as it began to be considered too toxic.
By 1917, Wagner-Juaregg’s theory about syphilis and fevers was becoming more credible – and one day a new opportunity presented itself when a wounded soldier, stricken with malaria and a related fever, was accidentally admitted to his psychiatric unit.
When his findings were published in 1918, Wagner-Juaregg’s so-called “fever therapy” swept the globe.
What Wagner-Juaregg did next was ethically deplorable by any standard: Before he allowed the soldier any quinine (the standard treatment for malaria at the time), Wagner-Juaregg took a small sample of the soldier’s blood and inoculated three syphilis patients with the sample, rubbing the blood on their open syphilitic blisters.
It’s unclear how well the malaria treatment worked for those three specific patients – but Wagner-Juaregg’s records show that in the span of one year, he inoculated a total of nine patients with malaria, for the sole purpose of inducing fevers, and six of them made a full recovery. Wagner-Juaregg’s treatment was so successful, in fact, that one of his inoculated patients, an actor who was unable to work due to his dementia, was eventually able to find work again and return to the stage. Two additional patients – a military officer and a clerk – recovered from their once-terminal illnesses and returned to their former careers as well.
When his findings were published in 1918, Wagner-Juaregg’s so-called “fever therapy” swept the globe. The treatment was hailed as a breakthrough – but it still had risks. Malaria itself had a mortality rate of about 15 percent at the time. Many people considered that to be a gamble worth taking, compared to dying a painful, protracted death from syphilis.
Malaria could also be effectively treated much of the time with quinine, whereas other fever-causing illnesses were not so easily treated. Triggering a fever by way of malaria specifically, therefore, became the standard of care.
Tens of thousands of people with syphilitic dementia would go on to be treated with fever therapy until the early 1940s, when a combination of Salvarsan and penicillin caused syphilis infections to decline. Eventually, neurosyphilis became rare, and then nearly unheard of.
Despite his contributions to medicine, it’s important to note that Wagner-Juaregg was most definitely not a person to idolize. In fact, he was an outspoken anti-Semite and proponent of eugenics, arguing that Jews were more prone to mental illness and that people who were mentally ill should be forcibly sterilized. (Wagner-Juaregg later became a Nazi sympathizer during Hitler’s rise to power even though, bizarrely, his first wife was Jewish.) Another problematic issue was that his fever therapy involved experimental treatments on many who, due to their cognitive issues, could not give informed consent.
Lack of consent was also a fundamental problem with the syphilis study at Tuskegee, appalling research that began just 14 years after Wagner-Juaregg published his “fever therapy” findings.
Still, despite his outrageous views, Wagner-Juaregg was awarded the Nobel Prize in Medicine or Physiology in 1927 – and despite some egregious human rights abuses, the miraculous “fever therapy” was partly responsible for taming one of the deadliest plagues in human history.