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."
Catching colds may help protect kids from Covid
A new study shows that the immune system's response to colds can help prepare it to defend against COVID-19 - but only in the very young.
A common cold virus causes the immune system to produce T cells that also provide protection against SARS-CoV-2, according to new research. The study, published last month in PNAS, shows that this effect is most pronounced in young children. The finding may help explain why most young people who have been exposed to the cold-causing coronavirus have not developed serious cases of COVID-19.
One curiosity stood out in the early days of the COVID-19 pandemic – why were so few kids getting sick. Generally young children and the elderly are the most vulnerable to disease outbreaks, particularly viral infections, either because their immune systems are not fully developed or they are starting to fail.
But solid information on the new infection was so scarce that many public health officials acted on the precautionary principle, assumed a worst-case scenario, and applied the broadest, most restrictive policies to all people to try to contain the coronavirus SARS-CoV-2.
One early thought was that lockdowns worked and kids (ages 6 months to 17 years) simply were not being exposed to the virus. So it was a shock when data started to come in showing that well over half of them carried antibodies to the virus, indicating exposure without getting sick. That trend grew over time and the latest tracking data from the CDC shows that 96.3 percent of kids in the U.S. now carry those antibodies.
Antibodies are relatively quick and easy to measure, but some scientists are exploring whether the reactions of T cells could serve as a more useful measure of immune protection.
But that couldn't be the whole story because antibody protection fades, sometimes as early as a month after exposure and usually within a year. Additionally, SARS-CoV-2 has been spewing out waves of different variants that were more resistant to antibodies generated by their predecessors. The resistance was so significant that over time the FDA withdrew its emergency use authorization for a handful of monoclonal antibodies with earlier approval to treat the infection because they no longer worked.
Antibodies got most of the attention early on because they are part of the first line response of the immune system. Antibodies can bind to viruses and neutralize them, preventing infection. They are relatively quick and easy to measure and even manufacture, but as SARS-CoV-2 showed us, often viruses can quickly evolve to become more resistant to them. Some scientists are exploring whether the reactions of T cells could serve as a more useful measure of immune protection.
Kids, colds and T cells
T cells are part of the immune system that deals with cells once they have become infected. But working with T cells is much more difficult, takes longer, and is more expensive than working with antibodies. So studies often lags behind on this part of the immune system.
A group of researchers led by Annika Karlsson at the Karolinska Institute in Sweden focuses on T cells targeting virus-infected cells and, unsurprisingly, saw that they can play a role in SARS-CoV-2 infection. Other labs have shown that vaccination and natural exposure to the virus generates different patterns of T cell responses.
The Swedes also looked at another member of the coronavirus family, OC43, which circulates widely and is one of several causes of the common cold. The molecular structure of OC43 is similar to its more deadly cousin SARS-CoV-2. Sometimes a T cell response to one virus can produce a cross-reactive response to a similar protein structure in another virus, meaning that T cells will identify and respond to the two viruses in much the same way. Karlsson looked to see if T cells for OC43 from a wide age range of patients were cross-reactive to SARS-CoV-2.
And that is what they found, as reported in the PNAS study last month; there was cross-reactive activity, but it depended on a person’s age. A subset of a certain type of T cells, called mCD4+,, that recognized various protein parts of the cold-causing virus, OC43, expressed on the surface of an infected cell – also recognized those same protein parts from SARS-CoV-2. The T cell response was lower than that generated by natural exposure to SARS-CoV-2, but it was functional and thus could help limit the severity of COVID-19.
“One of the most politicized aspects of our pandemic response was not accepting that children are so much less at risk for severe disease with COVID-19,” because usually young children are among the most vulnerable to pathogens, says Monica Gandhi, professor of medicine at the University of California San Francisco.
“The cross-reactivity peaked at age six when more than half the people tested have a cross-reactive immune response,” says Karlsson, though their sample is too small to say if this finding applies more broadly across the population. The vast majority of children as young as two years had OC43-specific mCD4+ T cell responses. In adulthood, the functionality of both the OC43-specific and the cross-reactive T cells wane significantly, especially with advanced age.
“Considering that the mortality rate in children is the lowest from ages five to nine, and higher in younger children, our results imply that cross-reactive mCD4+ T cells may have a role in the control of SARS-CoV-2 infection in children,” the authors wrote in their paper.
“One of the most politicized aspects of our pandemic response was not accepting that children are so much less at risk for severe disease with COVID-19,” because usually young children are among the most vulnerable to pathogens, says Monica Gandhi, professor of medicine at the University of California San Francisco and author of the book, Endemic: A Post-Pandemic Playbook, to be released by the Mayo Clinic Press this summer. The immune response of kids to SARS-CoV-2 stood our expectations on their head. “We just haven't seen this before, so knowing the mechanism of protection is really important.”
Why the T cell immune response can fade with age is largely unknown. With some viruses such as measles, a single vaccination or infection generates life-long protection. But respiratory tract infections, like SARS-CoV-2, cause a localized infection - specific to certain organs - and that response tends to be shorter lived than systemic infections that affect the entire body. Karlsson suspects the elderly might be exposed to these localized types of viruses less often. Also, frequent continued exposure to a virus that results in reactivation of the memory T cell pool might eventually result in “a kind of immunosenescence or immune exhaustion that is associated with aging,” Karlsson says. https://leaps.org/scientists-just-started-testing-a-new-class-of-drugs-to-slow-and-even-reverse-aging/particle-3 This fading protection is why older people need to be repeatedly vaccinated against SARS-CoV-2.
Policy implications
Following the numbers on COVID-19 infections and severity over the last three years have shown us that healthy young people without risk factors are not likely to develop serious disease. This latest study points to a mechanism that helps explain why. But the inertia of existing policies remains. How should we adjust policy recommendations based on what we know today?
The World Health Organization (WHO) updated their COVID-19 vaccination guidance on March 28. It calls for a focus on vaccinating and boosting those at risk for developing serious disease. The guidance basically shrugged its shoulders when it came to healthy children and young adults receiving vaccinations and boosters against COVID-19. It said the priority should be to administer the “traditional essential vaccines for children,” such as those that protect against measles, rubella, and mumps.
“As an immunologist and a mother, I think that catching a cold or two when you are a kid and otherwise healthy is not that bad for you. Children have a much lower risk of becoming severely ill with SARS-CoV-2,” says Karlsson. She has followed public health guidance in Sweden, which means that her young children have not been vaccinated, but being older, she has received the vaccine and boosters. Gandhi and her children have been vaccinated, but they do not plan on additional boosters.
The WHO got it right in “concentrating on what matters,” which is getting traditional childhood immunizations back on track after their dramatic decline over the last three years, says Gandhi. Nor is there a need for masking in schools, according to a study from the Catalonia region of Spain. It found “no difference in masking and spread in schools,” particularly since tracking data indicate that nearly all young people have been exposed to SARS-CoV-2.
Both researchers lament that public discussion has overemphasized the quickly fading antibody part of the immune response to SARS-CoV-2 compared with the more durable T cell component. They say developing an efficient measure of T cell response for doctors to use in the clinic would help to monitor immunity in people at risk for severe cases of COVID-19 compared with the current method of toting up potential risk factors.
In this week's Friday Five: The eyes are the windows to the soul - and biological aging?
Plus, what bean genes mean for health and the planet, a breathing practice that could lower levels of tau proteins in the brain, AI beats humans at assessing heart health, and the benefits of "nature prescriptions"
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 new scientific theories and progress to give you a therapeutic dose of inspiration headed into the weekend.
Listen on Apple | Listen on Spotify | Listen on Stitcher | Listen on Amazon | Listen on Google
Here are the stories covered this week:
- The eyes are the windows to the soul - and biological aging?
- What bean genes mean for health and the planet
- This breathing practice could lower levels of tau proteins
- AI beats humans at assessing heart health
- Should you get a nature prescription?