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 1945, almost two decades after Alexander Fleming discovered penicillin, he warned that as antibiotics use grows, they may lose their efficiency. He was prescient—the first case of penicillin resistance was reported two years later. Back then, not many people paid attention to Fleming’s warning. After all, the “golden era” of the antibiotics age had just began. By the 1950s, three new antibiotics derived from soil bacteria — streptomycin, chloramphenicol, and tetracycline — could cure infectious diseases like tuberculosis, cholera, meningitis and typhoid fever, among others.
Today, these antibiotics and many of their successors developed through the 1980s are gradually losing their effectiveness. The extensive overuse and misuse of antibiotics led to the rise of drug resistance. The livestock sector buys around 80 percent of all antibiotics sold in the U.S. every year. Farmers feed cows and chickens low doses of antibiotics to prevent infections and fatten up the animals, which eventually causes resistant bacterial strains to evolve. If manure from cattle is used on fields, the soil and vegetables can get contaminated with antibiotic-resistant bacteria. Another major factor is doctors overprescribing antibiotics to humans, particularly in low-income countries. Between 2000 to 2018, the global rates of human antibiotic consumption shot up by 46 percent.
In recent years, researchers have been exploring a promising avenue: the use of synthetic biology to engineer new bacteria that may work better than antibiotics. The need continues to grow, as a Lancet study linked antibiotic resistance to over 1.27 million deaths worldwide in 2019, surpassing HIV/AIDS and malaria. The western sub-Saharan Africa region had the highest death rate (27.3 people per 100,000).
Researchers warn that if nothing changes, by 2050, antibiotic resistance could kill 10 million people annually.
To make it worse, our remedy pipelines are drying up. Out of the 18 biggest pharmaceutical companies, 15 abandoned antibiotic development by 2013. According to the AMR Action Fund, venture capital has remained indifferent towards biotech start-ups developing new antibiotics. In 2019, at least two antibiotic start-ups filed for bankruptcy. As of December 2020, there were 43 new antibiotics in clinical development. But because they are based on previously known molecules, scientists say they are inadequate for treating multidrug-resistant bacteria. Researchers warn that if nothing changes, by 2050, antibiotic resistance could kill 10 million people annually.
The rise of synthetic biology
To circumvent this dire future, scientists have been working on alternative solutions using synthetic biology tools, meaning genetically modifying good bacteria to fight the bad ones.
From the time life evolved on earth around 3.8 billion years ago, bacteria have engaged in biological warfare. They constantly strategize new methods to combat each other by synthesizing toxic proteins that kill competition.
For example, Escherichia coli produces bacteriocins or toxins to kill other strains of E.coli that attempt to colonize the same habitat. Microbes like E.coli (which are not all pathogenic) are also naturally present in the human microbiome. The human microbiome harbors up to 100 trillion symbiotic microbial cells. The majority of them are beneficial organisms residing in the gut at different compositions.
The chemicals that these “good bacteria” produce do not pose any health risks to us, but can be toxic to other bacteria, particularly to human pathogens. For the last three decades, scientists have been manipulating bacteria’s biological warfare tactics to our collective advantage.
In the late 1990s, researchers drew inspiration from electrical and computing engineering principles that involve constructing digital circuits to control devices. In certain ways, every cell in living organisms works like a tiny computer. The cell receives messages in the form of biochemical molecules that cling on to its surface. Those messages get processed within the cells through a series of complex molecular interactions.
Synthetic biologists can harness these living cells’ information processing skills and use them to construct genetic circuits that perform specific instructions—for example, secrete a toxin that kills pathogenic bacteria. “Any synthetic genetic circuit is merely a piece of information that hangs around in the bacteria’s cytoplasm,” explains José Rubén Morones-Ramírez, a professor at the Autonomous University of Nuevo León, Mexico. Then the ribosome, which synthesizes proteins in the cell, processes that new information, making the compounds scientists want bacteria to make. “The genetic circuit remains separated from the living cell’s DNA,” Morones-Ramírez explains. When the engineered bacteria replicates, the genetic circuit doesn’t become part of its genome.
Highly intelligent by bacterial standards, some multidrug resistant V. cholerae strains can also “collaborate” with other intestinal bacterial species to gain advantage and take hold of the gut.
In 2000, Boston-based researchers constructed an E.coli with a genetic switch that toggled between turning genes on and off two. Later, they built some safety checks into their bacteria. “To prevent unintentional or deleterious consequences, in 2009, we built a safety switch in the engineered bacteria’s genetic circuit that gets triggered after it gets exposed to a pathogen," says James Collins, a professor of biological engineering at MIT and faculty member at Harvard University’s Wyss Institute. “After getting rid of the pathogen, the engineered bacteria is designed to switch off and leave the patient's body.”
Overuse and misuse of antibiotics causes resistant strains to evolve
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Seek and destroy
As the field of synthetic biology developed, scientists began using engineered bacteria to tackle superbugs. They first focused on Vibrio cholerae, which in the 19th and 20th century caused cholera pandemics in India, China, the Middle East, Europe, and Americas. Like many other bacteria, V. cholerae communicate with each other via quorum sensing, a process in which the microorganisms release different signaling molecules, to convey messages to its brethren. Highly intelligent by bacterial standards, some multidrug resistant V. cholerae strains can also “collaborate” with other intestinal bacterial species to gain advantage and take hold of the gut. When untreated, cholera has a mortality rate of 25 to 50 percent and outbreaks frequently occur in developing countries, especially during floods and droughts.
Sometimes, however, V. cholerae makes mistakes. In 2008, researchers at Cornell University observed that when quorum sensing V. cholerae accidentally released high concentrations of a signaling molecule called CAI-1, it had a counterproductive effect—the pathogen couldn’t colonize the gut.
So the group, led by John March, professor of biological and environmental engineering, developed a novel strategy to combat V. cholerae. They genetically engineered E.coli to eavesdrop on V. cholerae communication networks and equipped it with the ability to release the CAI-1 molecules. That interfered with V. cholerae progress. Two years later, the Cornell team showed that V. cholerae-infected mice treated with engineered E.coli had a 92 percent survival rate.
These findings inspired researchers to sic the good bacteria present in foods like yogurt and kimchi onto the drug-resistant ones.
Three years later in 2011, Singapore-based scientists engineered E.coli to detect and destroy Pseudomonas aeruginosa, an often drug-resistant pathogen that causes pneumonia, urinary tract infections, and sepsis. Once the genetically engineered E.coli found its target through its quorum sensing molecules, it then released a peptide, that could eradicate 99 percent of P. aeruginosa cells in a test-tube experiment. The team outlined their work in a Molecular Systems Biology study.
“At the time, we knew that we were entering new, uncharted territory,” says lead author Matthew Chang, an associate professor and synthetic biologist at the National University of Singapore and lead author of the study. “To date, we are still in the process of trying to understand how long these microbes stay in our bodies and how they might continue to evolve.”
More teams followed the same path. In a 2013 study, MIT researchers also genetically engineered E.coli to detect P. aeruginosa via the pathogen’s quorum-sensing molecules. It then destroyed the pathogen by secreting a lab-made toxin.
Probiotics that fight
A year later in 2014, a Nature study found that the abundance of Ruminococcus obeum, a probiotic bacteria naturally occurring in the human microbiome, interrupts and reduces V.cholerae’s colonization— by detecting the pathogen’s quorum sensing molecules. The natural accumulation of R. obeum in Bangladeshi adults helped them recover from cholera despite living in an area with frequent outbreaks.
The findings from 2008 to 2014 inspired Collins and his team to delve into how good bacteria present in foods like yogurt and kimchi can attack drug-resistant bacteria. In 2018, Collins and his team developed the engineered probiotic strategy. They tweaked a bacteria commonly found in yogurt called Lactococcus lactis to treat cholera.
Engineered bacteria can be trained to target pathogens when they are at their most vulnerable metabolic stage in the human gut. --José Rubén Morones-Ramírez.
More scientists followed with more experiments. So far, researchers have engineered various probiotic organisms to fight pathogenic bacteria like Staphylococcus aureus (leading cause of skin, tissue, bone, joint and blood infections) and Clostridium perfringens (which causes watery diarrhea) in test-tube and animal experiments. In 2020, Russian scientists engineered a probiotic called Pichia pastoris to produce an enzyme called lysostaphin that eradicated S. aureus in vitro. Another 2020 study from China used an engineered probiotic bacteria Lactobacilli casei as a vaccine to prevent C. perfringens infection in rabbits.
In a study last year, Ramírez’s group at the Autonomous University of Nuevo León, engineered E. coli to detect quorum-sensing molecules from Methicillin-resistant Staphylococcus aureus or MRSA, a notorious superbug. The E. coli then releases a bacteriocin that kills MRSA. “An antibiotic is just a molecule that is not intelligent,” says Ramírez. “On the other hand, engineered bacteria can be trained to target pathogens when they are at their most vulnerable metabolic stage in the human gut.”
Collins and Timothy Lu, an associate professor of biological engineering at MIT, found that engineered E. coli can help treat other conditions—such as phenylketonuria, a rare metabolic disorder, that causes the build-up of an amino acid phenylalanine. Their start-up Synlogic aims to commercialize the technology, and has completed a phase 2 clinical trial.
Circumventing the challenges
The bacteria-engineering technique is not without pitfalls. One major challenge is that beneficial gut bacteria produce their own quorum-sensing molecules that can be similar to those that pathogens secrete. If an engineered bacteria’s biosensor is not specific enough, it will be ineffective.
Another concern is whether engineered bacteria might mutate after entering the gut. “As with any technology, there are risks where bad actors could have the capability to engineer a microbe to act quite nastily,” says Collins of MIT. But Collins and Ramírez both insist that the chances of the engineered bacteria mutating on its own are virtually non-existent. “It is extremely unlikely for the engineered bacteria to mutate,” Ramírez says. “Coaxing a living cell to do anything on command is immensely challenging. Usually, the greater risk is that the engineered bacteria entirely lose its functionality.”
However, the biggest challenge is bringing the curative bacteria to consumers. Pharmaceutical companies aren’t interested in antibiotics or their alternatives because it’s less profitable than developing new medicines for non-infectious diseases. Unlike the more chronic conditions like diabetes or cancer that require long-term medications, infectious diseases are usually treated much quicker. Running clinical trials are expensive and antibiotic-alternatives aren’t lucrative enough.
“Unfortunately, new medications for antibiotic resistant infections have been pushed to the bottom of the field,” says Lu of MIT. “It's not because the technology does not work. This is more of a market issue. Because clinical trials cost hundreds of millions of dollars, the only solution is that governments will need to fund them.” Lu stresses that societies must lobby to change how the modern healthcare industry works. “The whole world needs better treatments for antibiotic resistance.”
Meet Dr. Renee Wegrzyn, the first Director of President Biden's new health agency, ARPA-H
In today’s podcast episode, I talk with Renee Wegrzyn, appointed by President Biden as the first director of a health agency created last year, the Advanced Research Projects Agency for Health, or ARPA-H. It’s inspired by DARPA, the agency that develops innovations for the Defense department and has been credited with hatching world-changing technologies such as ARPANET, which became the internet.
Time will tell if ARPA-H will lead to similar achievements in the realm of health. That’s what President Biden and Congress expect in return for funding ARPA-H at 2.5 billion dollars over three years.
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How will the agency figure out which projects to take on, especially with so many patient advocates for different diseases demanding moonshot funding for rapid progress?
I talked with Dr. Wegrzyn about the opportunities and challenges, what lessons ARPA-H is borrowing from Operation Warp Speed, how she decided on the first ARPA-H project that was announced recently, why a separate agency was needed instead of reforming HHS and the National Institutes of Health to be better at innovation, and how ARPA-H will make progress on disease prevention in addition to treatments for cancer, Alzheimer’s and diabetes, among many other health priorities.
Dr. Wegrzyn’s resume leaves no doubt of her suitability for this role. She was a program manager at DARPA where she focused on applying gene editing and synthetic biology to the goal of improving biosecurity. For her work there, she received the Superior Public Service Medal and, in case that wasn’t enough ARPA experience, she also worked at another ARPA that leads advanced projects in intelligence, called I-ARPA. Before that, she ran technical teams in the private sector working on gene therapies and disease diagnostics, among other areas. She has been a vice president of business development at Gingko Bioworks and headed innovation at Concentric by Gingko. Her training and education includes a PhD and undergraduate degree in applied biology from the Georgia Institute of Technology and she did her postdoc as an Alexander von Humboldt Fellow in Heidelberg, Germany.
Dr. Wegrzyn told me that she’s “in the hot seat.” The pressure is on for ARPA-H especially after the need and potential for health innovation was spot lit by the pandemic and the unprecedented speed of vaccine development. We'll soon find out if ARPA-H can produce gamechangers in health that are equivalent to DARPA’s creation of the internet.
Show links:
ARPA-H - https://arpa-h.gov/
Dr. Wegrzyn profile - https://arpa-h.gov/people/renee-wegrzyn/
Dr. Wegrzyn Twitter - https://twitter.com/rwegrzyn?lang=en
President Biden Announces Dr. Wegrzyn's appointment - https://www.whitehouse.gov/briefing-room/statement...
Leaps.org coverage of ARPA-H - https://leaps.org/arpa/
ARPA-H program for joints to heal themselves - https://arpa-h.gov/news/nitro/ -
ARPA-H virtual talent search - https://arpa-h.gov/news/aco-talent-search/
Dr. Renee Wegrzyn was appointed director of ARPA-H last October.