The Algorithm Will See You Now
There's a quiet revolution going on in medicine. It's driven by artificial intelligence, but paradoxically, new technology may put a more human face on healthcare.
AI's usefulness in healthcare ranges far and wide.
Artificial intelligence is software that can process massive amounts of information and learn over time, arriving at decisions with striking accuracy and efficiency. It offers greater accuracy in diagnosis, exponentially faster genome sequencing, the mining of medical literature and patient records at breathtaking speed, a dramatic reduction in administrative bureaucracy, personalized medicine, and even the democratization of healthcare.
The algorithms that bring these advantages won't replace doctors; rather, by offloading some of the most time-consuming tasks in healthcare, providers will be able to focus on personal interactions with patients—listening, empathizing, educating and generally putting the care back in healthcare. The relationship can focus on the alleviation of suffering, both the physical and emotional kind.
Challenges of Getting AI Up and Running
The AI revolution, still in its early phase in medicine, is already spurring some amazing advances, despite the fact that some experts say it has been overhyped. IBM's Watson Health program is a case in point. IBM capitalized on Watson's ability to process natural language by designing algorithms that devour data like medical articles and analyze images like MRIs and medical slides. The algorithms help diagnose diseases and recommend treatment strategies.
But Technology Review reported that a heavily hyped partnership with the MD Anderson Cancer Center in Houston fell apart in 2017 because of a lack of data in the proper format. The data existed, just not in a way that the voraciously data-hungry AI could use to train itself.
The hiccup certainly hasn't dampened the enthusiasm for medical AI among other tech giants, including Google and Apple, both of which have invested billions in their own healthcare projects. At this point, the main challenge is the need for algorithms to interpret a huge diversity of data mined from medical records. This can include everything from CT scans, MRIs, electrocardiograms, x-rays, and medical slides, to millions of pages of medical literature, physician's notes, and patient histories. It can even include data from implantables and wearables such as the Apple Watch and blood sugar monitors.
None of this information is in anything resembling a standard format across and even within hospitals, clinics, and diagnostic centers. Once the algorithms are trained, however, they can crunch massive amounts of data at blinding speed, with an accuracy that matches and sometimes even exceeds that of highly experienced doctors.
Genome sequencing, for example, took years to accomplish as recently as the early 2000s. The Human Genome Project, the first sequencing of the human genome, was an international effort that took 13 years to complete. In April of this year, Rady Children's Institute for Genomic Medicine in San Diego used an AI-powered genome sequencing algorithm to diagnose rare genetic diseases in infants in about 20 hours, according to ScienceDaily.
"Patient care will always begin and end with the doctor."
Dr. Stephen Kingsmore, the lead author of an article published in Science Translational Medicine, emphasized that even though the algorithm helped guide the treatment strategies of neonatal intensive care physicians, the doctor was still an indispensable link in the chain. "Some people call this artificial intelligence, we call it augmented intelligence," he says. "Patient care will always begin and end with the doctor."
One existing trend is helping to supply a great amount of valuable data to algorithms—the electronic health record. Initially blamed for exacerbating the already crushing workload of many physicians, the EHR is emerging as a boon for algorithms because it consolidates all of a patient's data in one record.
Examples of AI in Action Around the Globe
If you're a parent who has ever taken a child to the doctor with flulike symptoms, you know the anxiety of wondering if the symptoms signal something serious. Kang Zhang, M.D., Ph.D., the founding director of the Institute for Genomic Medicine at the University of California at San Diego, and colleagues developed an AI natural language processing model that used deep learning to analyze the EHRs of 1.3 million pediatric visits to a clinic in Guanzhou, China.
The AI identified common childhood diseases with about the same accuracy as human doctors, and it was even able to split the diagnoses into two categories—common conditions such as flu, and serious, life-threatening conditions like meningitis. Zhang has emphasized that the algorithm didn't replace the human doctor, but it did streamline the diagnostic process and could be used in a triage capacity when emergency room personnel need to prioritize the seriously ill over those suffering from common, less dangerous ailments.
AI's usefulness in healthcare ranges far and wide. In Uganda and several other African nations, AI is bringing modern diagnostics to remote villages that have no access to traditional technologies such as x-rays. The New York Times recently reported that there, doctors are using a pocket-sized, hand-held ultrasound machine that works in concert with a cell phone to image and diagnose everything from pneumonia (a common killer of children) to cancerous tumors.
The beauty of the highly portable, battery-powered device is that ultrasound images can be uploaded on computers so that physicians anywhere in the world can review them and weigh in with their advice. And the images are instantly incorporated into the patient's EHR.
Jonathan Rothberg, the founder of Butterfly Network, the Connecticut company that makes the device, told The New York Times that "Two thirds of the world's population gets no imaging at all. When you put something on a chip, the price goes down and you democratize it." The Butterfly ultrasound machine, which sells for $2,000, promises to be a game-changer in remote areas of Africa, South America, and Asia, as well as at the bedsides of patients in developed countries.
AI algorithms are rapidly emerging in healthcare across the U.S. and the world. China has become a major international player, set to surpass the U.S. this year in AI capital investment, the translation of AI research into marketable products, and even the number of often-cited research papers on AI. So far the U.S. is still the leader, but some experts describe the relationship between the U.S. and China as an AI cold war.
"The future of machine learning isn't sentient killer robots. It's longer human lives."
The U.S. Food and Drug Administration expanded its approval of medical algorithms from two in all of 2017 to about two per month throughout 2018. One of the first fields to be impacted is ophthalmology.
One algorithm, developed by the British AI company DeepMind (owned by Alphabet, the parent company of Google), instantly scans patients' retinas and is able to diagnose diabetic retinopathy without needing an ophthalmologist to interpret the scans. This means diabetics can get the test every year from their family physician without having to see a specialist. The Financial Times reported in March that the technology is now being used in clinics throughout Europe.
In Copenhagen, emergency service dispatchers are using a new voice-processing AI called Corti to analyze the conversations in emergency phone calls. The algorithm analyzes the verbal cues of callers, searches its huge database of medical information, and provides dispatchers with onscreen diagnostic information. Freddy Lippert, the CEO of EMS Copenhagen, notes that the algorithm has already saved lives by expediting accurate diagnoses in high-pressure situations where time is of the essence.
Researchers at the University of Nottingham in the UK have even developed a deep learning algorithm that predicts death more accurately than human clinicians. The algorithm incorporates data from a huge range of factors in a chronically ill population, including how many fruits and vegetables a patient eats on a daily basis. Dr. Stephen Weng, lead author of the study, published in PLOS ONE, said in a press release, "We found machine learning algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert."
New digital technologies are allowing patients to participate in their healthcare as never before. A feature of the new Apple Watch is an app that detects cardiac arrhythmias and even produces an electrocardiogram if an abnormality is detected. The technology, approved by the FDA, is helping cardiologists monitor heart patients and design interventions for those who may be at higher risk of a cardiac event like a stroke.
If having an algorithm predict your death sends a shiver down your spine, consider that algorithms may keep you alive longer. In 2018, technology reporter Tristan Greene wrote for Medium that "…despite the unending deluge of panic-ridden articles declaring AI the path to apocalypse, we're now living in a world where algorithms save lives every day. The future of machine learning isn't sentient killer robots. It's longer human lives."
The Risks of AI Compiling Your Data
To be sure, the advent of AI-infused medical technology is not without its risks. One risk is that the use of AI wearables constantly monitoring our vital signs could turn us into a nation of hypochondriacs, racing to our doctors every time there's a blip in some vital sign. Such a development could stress an already overburdened system that suffers from, among other things, a shortage of doctors and nurses. Another risk has to do with the privacy protections on the massive repository of intimately personal information that AI will have on us.
In an article recently published in the Journal of the American Medical Association, Australian researcher Kit Huckvale and colleagues examined the handling of data by 36 smartphone apps that assisted people with either depression or smoking cessation, two areas that could lend themselves to stigmatization if they fell into the wrong hands.
Out of the 36 apps, 33 shared their data with third parties, despite the fact that just 25 of those apps had a privacy policy at all and out of those, only 23 stated that data would be shared with third parties. The recipients of all that data? It went almost exclusively to Facebook and Google, to be used for advertising and marketing purposes. But there's nothing to stop it from ending up in the hands of insurers, background databases, or any other entity.
Even when data isn't voluntarily shared, any digital information can be hacked. EHRs and even wearable devices share the same vulnerability as any other digital record or device. Still, the promise of AI to radically improve efficiency and accuracy in healthcare is hard to ignore.
AI Can Help Restore Humanity to Medicine
Eric Topol, director of the Scripps Research Translational Institute and author of the new book Deep Medicine, says that AI gives doctors and nurses the most precious gift of all: time.
Topol welcomes his patients' use of the Apple Watch cardiac feature and is optimistic about the ways that AI is revolutionizing medicine. He says that the watch helps doctors monitor how well medications are working and has already helped to prevent strokes. But in addition to that, AI will help bring the humanity back to a profession that has become as cold and hard as a stainless steel dissection table.
"When I graduated from medical school in the 1970s," he says, "you had a really intimate relationship with your doctor." Over the decades, he has seen that relationship steadily erode as medical organizations demanded that doctors see more and more patients within ever-shrinking time windows.
"Doctors have no time to think, to communicate. We need to restore the mission in medicine."
In addition to that, EHRs have meant that doctors and nurses are getting buried in paperwork and administrative tasks. This is no doubt one reason why a recent study by the World Health Organization showed that worldwide, about 50 percent of doctors suffer from burnout. People who are utterly exhausted make more mistakes, and medical clinicians are no different from the rest of us. Only medical mistakes have unacceptably high stakes. According to its website, Johns Hopkins University recently announced that in the U.S. alone, 250,000 people die from medical mistakes each year.
"Doctors have no time to think, to communicate," says Topol. "We need to restore the mission in medicine." AI is giving doctors more time to devote to the thing that attracted them to medicine in the first place—connecting deeply with patients.
There is a real danger at this juncture, though, that administrators aware of the time-saving aspects of AI will simply push doctors to see more patients, read more tests, and embrace an even more crushing workload.
"We can't leave it to the administrators to just make things worse," says Topol. "Now is the time for doctors to advocate for a restoration of the human touch. We need to stand up for patients and for the patient-doctor relationship."
AI could indeed be a game changer, he says, but rather than squander the huge benefits of more time, "We need a new equation going forward."
In June 2012, Kirstie Ennis was six months into her second deployment to Afghanistan and recently promoted to sergeant. The helicopter gunner and seven others were three hours into a routine mission of combat resupplies and troop transport when their CH-53D helicopter went down hard.
Miraculously, all eight people onboard survived, but Ennis' injuries were many and severe. She had a torn rotator cuff, torn labrum, crushed cervical discs, facial fractures, deep lacerations and traumatic brain injury. Despite a severely fractured ankle, doctors managed to save her foot, for a while at least.
In November 2015, after three years of constant pain and too many surgeries to count, Ennis relented. She elected to undergo a lower leg amputation but only after she completed the 1,000-mile, 72-day Walking with the Wounded journey across the UK.
On Veteran's Day of that year, on the other side of the country, orthopedic surgeon Cato Laurencin announced a moonshot challenge he was setting out to achieve on behalf of wounded warriors like Ennis: the Hartford Engineering A Limb (HEAL) Project.
Laurencin, who is a University of Connecticut professor of chemical, materials and biomedical engineering, teamed up with experts in tissue bioengineering and regenerative medicine from Harvard, Columbia, UC Irvine and SASTRA University in India. Laurencin and his colleagues at the Connecticut Convergence Institute for Translation in Regenerative Engineering made a bold commitment to regenerate an entire limb within 15 years – by the year 2030.
Dr. Cato Laurencin pictured in his office at UConn.
Photo Credit: UConn
Regenerative Engineering -- A Whole New Field
Limb regeneration in humans has been a medical and scientific fascination for decades, with little to show for the effort. However, Laurencin believes that if we are to reach the next level of 21st century medical advances, this puzzle must be solved.
An estimated 185,000 people undergo upper or lower limb amputation every year. Despite the significant advances in electromechanical prosthetics, these individuals still lack the ability to perform complex functions such as sensation for tactile input, normal gait and movement feedback. As far as Laurencin is concerned, the only clinical answer that makes sense is to regenerate a whole functional limb.
Laurencin feels other regeneration efforts were hampered by their siloed research methods with chemists, surgeons, engineers all working separately. Success, he argues, requires a paradigm shift to a trans-disciplinary approach that brings together cutting-edge technologies from disparate fields such as biology, material sciences, physical, chemical and engineering sciences.
As the only surgeon ever inducted into the academies of Science, Medicine and Innovation, Laurencin is uniquely suited for the challenge. He is regarded as the founder of Regenerative Engineering, defined as the convergence of advanced materials sciences, stem cell sciences, physics, developmental biology and clinical translation for the regeneration of complex tissues and organ systems.
But none of this is achievable without early clinician participation across scientific fields to develop new technologies and a deeper understanding of how to harness the body's innate regenerative capabilities. "When I perform a surgical procedure or something is torn or needs to be repaired, I count on the body being involved in regenerating tissue," he says. "So, understanding how the body works to regenerate itself and harnessing that ability is an important factor for the regeneration process."
The Birth of the Vision
Laurencin's passion for regeneration began when he was a sports medicine fellow at Cornell University Medical Center in the early 1990s. There he saw a significant number of injuries to the anterior cruciate ligament (ACL), the major ligament that stabilizes the knee. He believed he could develop a better way to address those injuries using biomaterials to regenerate the ligament. He sketched out a preliminary drawing on a napkin one night over dinner. He has spent the next 30 years regenerating tissues, including the patented L-C ligament.
As chair of Orthopaedic Surgery at the University of Virginia during the peak of the wars in Iraq and Afghanistan, Laurencin treated military personnel who survived because of improved helmets, body armor and battlefield medicine but were left with more devastating injuries, including traumatic brain injuries and limb loss.
"I was so honored to care for them and I so admired their steadfast courage that I became determined to do something big for them," says Laurencin.
When he tells people about his plans to regrow a limb, he gets a lot of eye rolls, which he finds amusing but not discouraging. Growing bone cells was relatively new when he was first focused on regenerating bone in 1987 at MIT; in 2007 he was well on his way to regenerating ligaments at UVA when many still doubted that ligaments could even be reconstructed. He and his team have already regenerated torn rotator cuff tendons and ACL ligaments using a nano-textured fabric seeded with stem cells.
Even as a finalist for the $4 million NIH Pioneer Award for high-risk/high-reward research, he faced a skeptical scientific audience in 2014. "They said, 'Well what do you plan to do?' I said 'I plan to regenerate a whole limb in people.' There was a lot of incredulousness. They stared at me and asked a lot of questions. About three days later, I received probably the best score I've ever gotten on an NIH grant."
In the Thick of the Science
Humans are born with regenerative abilities--two-year-olds have regrown fingertips--but lose that ability with age. Salamanders are the only vertebrates that can regenerate lost body parts as adults; axolotl, the rare Mexican salamander, can grow extra limbs.
The axolotl is important as a model organism because it is a four-footed vertebrate with a similar body plan to humans. Mapping the axolotl genome in 2018 enhanced scientists' genetic understanding of their evolution, development, and regeneration. Being easy to breed in captivity allowed the HEAL team to closely study these amphibians and discover a new cell type they believe may shed light on how to mimic the process in humans.
"Whenever limb regeneration takes place in the salamander, there is a huge amount of something called heparan sulfate around that area," explains Laurencin. "We thought, 'What if this heparan sulfate is the key ingredient to allowing regeneration to take place?' We found these groups of cells that were interspersed in tissues during the time of regeneration that seemed to have connections to each other that expressed this heparan sulfate."
Called GRID (Groups that are Regenerative, Interspersed and Dendritic), these cells were also recently discovered in mice. While GRID cells don't regenerate as well in mice as in salamanders, finding them in mammals was significant.
"If they're found in mice. we might be able to find these in humans in some form," Laurencin says. "We think maybe it will help us figure out regeneration or we can create cells that mimic what grid cells do and create an artificial grid cell."
What Comes Next?
Laurencin and his team have individually engineered and made every single tissue in the lower limb, including bone, cartilage, ligament, skin, nerve, blood vessels. Regenerating joints and joint tissue is the next big mile marker, which Laurencin sees as essential to regenerating a limb that functions and performs in the way he envisions.
"Using stem cells and amnion tissue, we can regenerate joints that are damaged, and have severe arthritis," he says. "We're making progress on all fronts, and making discoveries we believe are going to be helping people along the way."
That focus and advancement is vital to Ennis. After laboring over the decision to have her leg amputated below the knee, she contracted MRSA two weeks post-surgery. In less than a month, she went from a below-the-knee-amputee to a through-the-knee amputee to an above-the-knee amputee.
"A below-the-knee amputation is night-and-day from above-the-knee," she said. "You have to relearn everything. You're basically a toddler."
Kirstie Ennis pictured in July 2020.
Photo Credit: Ennis' Instagram
The clock is ticking on the timeline Laurencin set for himself. Nine years might seem like forever if you're doing time but it might appear fleeting when you're trying to create something that's never been done before. But Laurencin isn't worried. He's convinced time is on his side.
"Every week, I receive an email or a call from someone, maybe a mother whose child has lost a finger or I'm in communication with a disabled American veteran who wants to know how the progress is going. That energizes me to continue to work hard to try to create these sorts of solutions because we're talking about people and their lives."
He devotes about 60 hours a week to the project and the roughly 100 students, faculty and staff who make up the HEAL team at the Convergence Institute seem acutely aware of what's at stake and appear equally dedicated.
"We're in the thick of the science in terms of making this happen," says Laurencin. "We've moved from making the impossible possible to making the possible a reality. That's what science is all about."
7 Reasons Why We Should Not Need Boosters for COVID-19
There are at least 7 reasons why immunity after vaccination or infection with COVID-19 should likely be long-lived. If durable, I do not think boosters will be necessary in the future, despite CEOs of pharmaceutical companies (who stand to profit from boosters) messaging that they may and readying such boosters. To explain these reasons, let's orient ourselves to the main components of the immune system.
There are two major arms of the immune system: B cells (which produce antibodies) and T cells (which are formed specifically to attack and kill pathogens). T cells are divided into two types, CD4 cells ("helper" T cells) and CD8 cells ("cytotoxic" T cells).
Each arm, once stimulated by infection or vaccine, should hopefully make "memory" banks. So if the body sees the pathogen in the future, these defenses should come roaring back to attack the virus and protect you from getting sick. Plenty of research in COVID-19 indicates a likely long-lasting response to the vaccine or infection. Here are seven of the most compelling reasons:
REASON 1: Memory B Cells Are Produced By Vaccines and Natural Infection
In one study, 12 volunteers who had never had Covid-19--and were fully vaccinated with two Pfizer/BioNTech shots-- underwent biopsies of their lymph nodes. This is where memory B cells are stored in places called "germinal centers". The biopsies were performed three, four, six, and seven weeks after the first mRNA vaccine shot, and were stained to reveal that germinal center memory B cells in the lymph nodes increased in concentration over time.
Natural infection also generates memory B cells. Even after antibody levels wane over time, strong memory B cells were detected in the blood of individuals six and eight months after infection in different studies. Indeed, the half-lives of the memory B cells seen in the study examining patients 8 months after COVID-19 led the authors to conclude that "B cell memory to SARS-CoV-2 was robust and is likely long-lasting." Reason #2 tells us that memory B cells can be active for a very long time indeed.
REASON #2: Memory B Cells Can Produce Neutralizing Antibodies If They See Infection Again Decades Later
Demonstrated production of memory B cells after vaccination or natural infection with COVID-19 is so important because memory B cells, once generated, can be activated to produce high levels of neutralizing antibodies against the pathogen even if encountered many years after the initial exposure. In one amazing study (published in 2008), researchers isolated memory B cells against the 1918 flu strain from the blood of 32 individuals aged 91-101 years. These people had been born on or before 1915 and had survived that pandemic.
Their memory B cells, when exposed to the 1918 flu strain in a test tube, generated high levels of neutralizing antibodies against the virus -- antibodies that then protected mice from lethal infection with this deadly strain. The ability of memory B cells to produce complex antibody responses against an infection nine decades after exposure speaks to their durability.
REASON #3: Vaccines or Natural Infection Trigger Strong Memory T Cell Immunity
All of the trials of the major COVID-19 vaccine candidates measured strong T cell immunity following vaccination, most often assessed by measuring SARS-CoV-2 specific T cells in the phase I/II safety and immunogenicity studies. There are a number of studies that demonstrate the production of strong T cell immunity to COVID-19 after natural infection as well, even when the infection was mild or asymptomatic.
The same study that showed us robust memory B cell production 8 months after natural infection also demonstrated strong and sustained memory T cell production. In fact, the half-lives of the memory T cells in this cohort were long (~125-225 days for CD8+ and ~94-153 days for CD4+ T cells), comparable to the 123-day half-life observed for memory CD8+ T cells after yellow fever immunization (a vaccine usually given once over a lifetime).
A recent study of individuals recovered from COVID-19 show that the initial T cells generated by natural infection mature and differentiate over time into memory T cells that will be "put in the bank" for sustained periods.
REASON #4: T Cell Immunity Following Vaccinations for Other Infections Is Long-Lasting
Last year, we were fortunate to be able to measure how T cell immunity is generated by COVID-19 vaccines, which was not possible in earlier eras when vaccine trials were done for other infections (such as measles, mumps, rubella, pertussis, diphtheria). Antibodies are just the "tip of the iceberg" when assessing the response to vaccination, but were the only arm of the immune response that could be measured following vaccination in the past.
Measuring pathogen-specific T cell responses takes sophisticated technology. However, T cell responses, when assessed years after vaccination for other pathogens, has been shown to be long-lasting. For example, in one study of 56 volunteers who had undergone measles vaccination when they were much younger, strong CD8 and CD4 cell responses to vaccination could be detected up to 34 years later.
REASON #5: T Cell Immunity to Related Coronaviruses That Caused Severe Disease is Long-Lasting
SARS-CoV-2 is a coronavirus that causes severe disease, unlike coronaviruses that cause the common cold. Two other coronaviruses in the recent past caused severe disease, specifically Severely Acute Respiratory Distress Syndrome (SARS) in late 2002-2003 and Middle East Respiratory Syndrome (MERS) in 2011.
A study performed in 2020 demonstrated that the blood of 23 recovered SARS patients possess long-lasting memory T cells that were still reactive to SARS 17 years after the outbreak in 2003. Many scientists expect that T cell immunity to SARS-CoV-2 will be equally durable to that of its cousin.
REASON #6: T Cell Responses from Vaccination and Natural Infection With the Ancestral Strain of COVID-19 Are Robust Against Variants
Even though antibody responses from vaccination may be slightly lower against various COVID-19 variants of concern that have emerged in recent months, T cell immunity after vaccination has been shown to be unperturbed by mutations in the spike protein (in the variants). For instance, T cell responses after mRNA vaccines maintained strong activity against different variants (including P.1 Brazil variant, B.1.1.7 UK variant, B.1.351 South Africa variant and the CA.20.C California variant) in a recent study.
Another study showed that the vaccines generated robust T cell immunity that was unfazed by different variants, including B.1.351 and B.1.1.7. The CD4 and CD8 responses generated after natural infection are equally robust, showing activity against multiple "epitopes" (little segments) of the spike protein of the virus. For instance, CD8 cells responds to 52 epitopes and CD4 cells respond to 57 epitopes across the spike protein, so that a few mutations in the variants cannot knock out such a robust and in-breadth T cell response. Indeed, a recent paper showed that mRNA vaccines were 97.4 percent effective against severe COVID-19 disease in Qatar, even when the majority of circulating virus there was from variants of concern (B.1.351 and B.1.1.7).
REASON #7: Coronaviruses Don't Mutate Quickly Like Influenza, Which Requires Annual Booster Shots
Coronaviruses are RNA viruses, like influenza and HIV (which is actually a retrovirus), but do not mutate as quickly as either one. The reason that coronaviruses don't mutate very rapidly is that their replicating mechanism (polymerase) has a strong proofreading mechanism: If the virus mutates, it usually goes back and self-corrects. Mutations can arise with high rates of replication when transmission is very frequent -- as has been seen in recent months with the emergence of SARS-CoV-2 variants during surges. However, the COVID-19 virus will not be mutating like this when we tamp down transmission with mass vaccination.
In conclusion, I and many of my infectious disease colleagues expect the immunity from natural infection or vaccination to COVID-19 to be durable. Let's put discussion of boosters aside and work hard on global vaccine equity and distribution since the pandemic is not over until it is over for us all.