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."
Scientists search for a universal coronavirus vaccine
Stephen Zeichner, an infectious disease specialist at the University of Virginia Medical Center, has made progress with an early stage universal coronavirus vaccine.
The Covid-19 pandemic had barely begun when VBI Vaccines, a biopharmaceutical company based in Cambridge, Massachusetts, initiated their search for a universal coronavirus vaccine.
It was March 2020, and while most pharmaceutical companies were scrambling to initiate vaccine programs which specifically targeted the SARS-CoV-2 virus, VBI’s executives were already keen to look at the broader picture.
Having observed the SARS and MERS coronavirus outbreaks over the last two decades, Jeff Baxter, CEO of VBI Vaccines, was aware that SARS-CoV-2 is unlikely to be the last coronavirus to move from an animal host into humans. “It's absolutely apparent that the future is to create a vaccine which gives more broad protection against not only pre-existing coronaviruses, but those that will potentially make the leap into humans in future,” says Baxter.
It was a prescient decision. Over the last two years, more biotechs and pharma companies have joined the search to find a vaccine which might be able to protect against all coronaviruses, along with dozens of academic research groups. Last September, the US National Institutes of Health dedicated $36 million specifically to pan-coronavirus vaccine research, while the global Coalition for Epidemic Preparedness Innovations (CEPI) has earmarked $200 million towards the effort.
Until October 2021, the very concept of whether it might be
theoretically possible to vaccinate against multiple coronaviruses remained an open question. But then a groundbreaking study renewed optimism.
The emergence of new variants of Covid-19 over the past year, particularly the highly mutated Omicron variant, has added greater impetus to find broader spectrum vaccines. But until October 2021, the very concept of whether it might be theoretically possible to vaccinate against multiple coronaviruses remained an open question. After all, scientists have spent decades trying to develop a similar vaccine for influenza with little success.
But then a groundbreaking study from renowned virologist Linfa Wang, who runs the emerging infectious diseases program at Duke-National University of Singapore Medical School, provided renewed optimism.
Wang found that eight SARS survivors who had been injected with the Pfizer/BioNTech Covid-19 vaccine had neutralising antibodies in their blood against SARS, the Alpha, Beta and Delta variants of SARS-CoV-2, and five other coronaviruses which reside in bats and pangolins. He concluded that the combination of past coronavirus infection, and immunization with a messenger RNA vaccine, had resulted in a wider spectrum of protection than might have been expected.
“This is a significant study because it showed that pre-existing immunity to one coronavirus could help with the elicitation of cross-reactive antibodies when immunizing with a second coronavirus,” says Kevin Saunders, Director of Research at the Duke Human Vaccine Institute in North Carolina, which is developing a universal coronavirus vaccine. “It provides a strategy to perhaps broaden the immune response against coronaviruses.”
In the next few months, some of the first data is set to emerge looking at whether this kind of antibody response could be elicited by a single universal coronavirus vaccine. In April 2021, scientists at the Walter Reed Army Institute of Research in Silver Spring, Maryland, launched a Phase I clinical trial of their vaccine, with a spokesman saying that it was successful, and the full results will be announced soon.
The Walter Reed researchers have already released preclinical data, testing the vaccine in non-human primates where it was found to have immunising capabilities against a range of Covid-19 variants as well as the original SARS virus. If the Phase I trial displays similar efficacy, a larger Phase II trial will begin later this year.
Two different approaches
Broadly speaking, scientists are taking two contrasting approaches to the task of finding a universal coronavirus vaccine. The Walter Reed Army Institute of Research, VBI Vaccines – who plan to launch their own clinical trial in the summer – and the Duke Human Vaccine Institute – who are launching a Phase I trial in early 2023 – are using a soccer-ball shaped ferritin nanoparticle studded with different coronavirus protein fragments.
VBI Vaccines is looking to elicit broader immune responses by combining SARS, SARS-CoV-2 and MERS spike proteins on the same nanoparticle. Dave Anderson, chief scientific officer at VBI Vaccines, explains that the idea is that by showing the immune system these three spike proteins at the same time, it can help train it to identify and respond to subtle differences between coronavirus strains.
The Duke Human Vaccine Institute is utilising the same method, but rather than including the entire spike proteins from different coronaviruses, they are only including the receptor binding domain (RBD) fragment from each spike protein. “We designed our vaccine to focus the immune system on a site of vulnerability for the virus, which is the receptor binding domain,” says Saunders. “Since the RBD is small, arraying multiple RBDs on a nanoparticle is a straight-forward approach. The goal is to generate immunity to many different subgenuses of viruses so that there will be cross-reactivity with new or unknown coronaviruses.”
But the other strategy is to create a vaccine which contains regions of the viral protein structure which are conserved between all coronavirus strains. This is something which scientists have tried to do for a universal influenza vaccine, but it is thought to be more feasible for coronaviruses because they mutate at a slower rate and are more constrained in the ways that they can evolve.
DIOSynVax, a biotech based in Cambridge, United Kingdom, announced in a press release earlier this month that they are partnering with CEPI to use their computational predictive modelling techniques to identify common structures between all of the SARS coronaviruses which do not mutate, and thus present good vaccine targets.
Stephen Zeichner, an infectious disease specialist at the University of Virginia Medical Center, has created an early stage vaccine using the fusion peptide region – another part of the coronavirus spike protein that aids the virus’s entry into host cells – which so far appears to be highly conserved between all coronaviruses.
So far Zeichner has trialled this version of the vaccine in pigs, where it provided protection against a different coronavirus called porcine epidemic diarrhea virus, which he described as very promising as this virus is from a different family called alphacoronaviruses, while SARS-CoV-2 is a betacoronavirus.
“If a betacoronavirus fusion peptide vaccine designed from SARS-CoV-2 can protect pigs against clinical disease from an alphacoronavirus, then that suggests that an analogous vaccine would enable broad protection against many, many different coronaviruses,” he says.
The road ahead
But while some of the early stage results are promising, researchers are fully aware of the scale of the challenge ahead of them. Although CEPI have declared an aim of having a licensed universal coronavirus vaccine available by 2024-2025, Zeichner says that such timelines are ambitious in the extreme.
“I was incredibly impressed at the speed at which the mRNA coronavirus vaccines were developed for SARS-CoV-2,” he says. “That was faster than just about anybody anticipated. On the other hand, I think a universal coronavirus vaccine is more equivalent to the challenge of developing an HIV vaccine and we're 35 years into that effort without success. We know a lot more now than before, and maybe it will be easier than we think. But I think the route to a universal vaccine is harder than an individual vaccine, so I wouldn’t want to put money on a timeline prediction.”
The major challenge for scientists is essentially designing a vaccine for a future threat which is not even here yet. As such, there are no guidelines on what safety data would be required to license such a vaccine, and how researchers can demonstrate that it truly provides efficacy against all coronaviruses, even those which have not yet jumped to humans.
The teams working on this problem have already devised some ingenious ways of approaching the challenge. VBI Vaccines have taken the genetic sequences of different coronaviruses found in bats and pangolins, from publicly available databases, and inserted them into what virologists call a pseudotype virus – one which has been engineered so it does not have enough genetic material to replicate.
This has allowed them to test the neutralising antibodies that their vaccine produces against these coronaviruses in test tubes, under safe lab conditions. “We have literally just been ordering the sequences, and making synthetic viruses that we can use to test the antibody responses,” says Anderson.
However, some scientists feel that going straight to a universal coronavirus vaccine is likely to be too complex. Instead they say that we should aim for vaccines which are a little more specific. Pamela Bjorkman, a structural biologist at the California Institute of Technology, suggests that pan-coronavirus vaccines which protect against SARS-like betacoronaviruses such as SARS or SARS-CoV-2, or MERS-like betacoronaviruses, may be more realistic.
“I think a vaccine to protect against all coronaviruses is likely impossible since there are so many varieties,” she says. “Perhaps trying to narrow down the scope is advisable.”
But if the mission to develop a universal coronavirus vaccine does succeed, it will be one of the most remarkable feats in the annals of medical science. In January, US chief medical advisor Anthony Fauci urged for greater efforts to be devoted towards this goal, one which scientists feel would be the biological equivalent of the race to develop the first atomic bomb
“The development of an effective universal coronavirus vaccine would be equally groundbreaking, as it would have global applicability and utility,” says Saunders. “Coronaviruses have caused multiple deadly outbreaks, and it is likely that another outbreak will occur. Having a vaccine that prevents death from a future outbreak would be a tremendous achievement in global health.”
He agrees that it will require creativity on a remarkable scale: “The universal coronavirus vaccine will also require ingenuity and perseverance comparable to that needed for the Manhattan project.”
This month, Matt Fuchs becomes the new Editor-in-Chief of Leaps.org.
This month, Kira Peikoff passes the torch to me as editor-in-chief of Leaps.org. I’m excited to assume leadership of this important platform.
Leaps.org caught my eye back in 2018. I was in my late 30s and just starting to wake up to the reality that the people I care most about were getting older and more vulnerable to health problems. At the same time, three critical shifts were becoming impossible to ignore. First, the average age in the U.S. is getting older, a trend known as the “gray tsunami.” Second, healthcare expenses are escalating and becoming unsustainable. And third, our sedentary, stress-filled lifestyles are leading to devastating consequences.
These trends pointed to a future filled with disease, suffering and economic collapse. But whenever I visited Leaps.org, my outlook turned from gloomy to solution-oriented. I became just as fascinated in a fourth trend, one that stands to revolutionize our world: rapid, mind-bending innovations in health and medicine.
Brain atlases, genome sequencing and editing, AI, protein mapping, synthetic biology, 3-D printing—these technologies are yielding new opportunities for health, longevity and human thriving. COVID-19 has caused many setbacks, but it has accelerated scientific breakthroughs. History suggests we will see even more innovation—in digital health and virtual first care, for example—after the pandemic.
In 2020, I began covering these developments with articles for Leaps.org about clocks that measure biological aging, gene therapies for cystic fibrosis, and other seemingly futuristic concepts that are transforming the present. I wrote about them partly because I think most people aren’t aware of them—and meaningful progress can’t happen without public engagement. A broader set of stakeholders and society at large, not just the experts, must inform these changes to ensure that they reflect our values and ethics. Everyone should get the chance to participate in the conversation—and they must have the opportunity to benefit equally from the innovations we decide to move forward with. By highlighting cutting-edge advances, Leaps.org is helping to realize this important goal.
Meanwhile, as I wrote freelance pieces on health and wellness for outlets such as the Washington Post and Time Magazine, I kept seeing an intersect between the breakthroughs in research labs and our expanding knowledge about the science of well-being. Take, for example, emerging technologies designed to stop illnesses in their tracks and new research on the benefits of taking in natural daylight. These two areas, lab innovations and healthy lifestyles, both shift the focus from disease treatment to disease prevention and optimal health. It’s the only sensible, financially feasible way forward.
When Kira suggested that I consider a leadership role with Leaps.org, it struck me how much the platform’s ideals have informed my own perspectives. The frontpage gore of mainstream media outlets can feel like a daily dose of pessimism, with cynicism sometimes dressed up as wisdom. Leaps.org’s world view is rooted in something very different: rational optimism about the present moment and the possibility of human flourishing.
That’s why I’m proud to lead this platform, including our podcast, Making Sense of Science, and hope you’ll keep coming to Leaps.org to learn and join the conversation about scientific gamechangers through our sponsored events, our popular Instagram account and other social channels. Think critically about the breakthroughs and their ethical challenges. Help usher in the health and prosperity that could be ours if we stay open-minded to it.
Yours truly,
Matt Fuchs
Editor-in-Chief