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.
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 November 2020, messenger RNA catapulted into the public consciousness when the first COVID-19 vaccines were authorized for emergency use. Around the same time, an equally groundbreaking yet relatively unheralded application of mRNA technology was taking place at a London hospital.
Over the past two decades, there's been increasing interest in harnessing mRNA — molecules present in all of our cells that act like digital tape recorders, copying instructions from DNA in the cell nucleus and carrying them to the protein-making structures — to create a whole new class of therapeutics.
Scientists realized that artificial mRNA, designed in the lab, could be used to instruct our cells to produce certain antibodies, turning our bodies into vaccine-making factories, or to recognize and attack tumors. More recently, researchers recognized that mRNA could also be used to make another groundbreaking technology far more accessible to more patients: gene editing. The gene-editing tool CRISPR has generated plenty of hype for its potential to cure inherited diseases. But delivering CRISPR to the body is complicated and costly.
"Most gene editing involves taking cells out of the patient, treating them and then giving them back, which is an extremely expensive process," explains Drew Weissman, professor of medicine at the University of Pennsylvania, who was involved in developing the mRNA technology behind the COVID-19 vaccines.
But last November, a Massachusetts-based biotech company called Intellia Therapeutics showed it was possible to use mRNA to make the CRISPR system inside the body, eliminating the need to extract cells out of the body and edit them in a lab. Just as mRNA can instruct our cells to produce antibodies against a viral infection, it can also teach them to produce the two molecular components that make up CRISPR — a guide molecule and a cutting protein — to snip out a problem gene.
"The pandemic has really shown that not only are mRNA approaches viable, they could in certain circumstances be vastly superior to more traditional technologies."
In Intellia's London-based clinical trial, the company applied this for the first time in a patient with a rare inherited liver disease known as hereditary transthyretin amyloidosis with polyneuropathy. The disease causes a toxic protein to build up in a person's organs and is typically fatal. In a company press release, Intellia's president and CEO John Leonard swiftly declared that its mRNA-based CRISPR therapy could usher in a "new era of potential genome editing cures."
Weissman predicts that turning CRISPR into an affordable therapy will become the next major frontier for mRNA over the coming decade. His lab is currently working on an mRNA-based CRISPR treatment for sickle cell disease. More than 300,000 babies are born with sickle cell every year, mainly in lower income nations.
"There is a FDA-approved cure, but it involves taking the bone marrow out of the person, and then giving it back which is prohibitively expensive," he says. It also requires a patient to have a matched bone marrow done. "We give an intravenous injection of mRNA lipid nanoparticles that target CRISPR to the bone marrow stem cells in the patient, which is easy, and much less expensive."
Meanwhile, the overwhelming success of the COVID-19 vaccines has focused attention on other ways of using mRNA to bolster the immune system against threats ranging from other infectious diseases to cancer.
The practicality of mRNA vaccines – relatively small quantities are required to induce an antibody response – coupled with their adaptable design, mean companies like Moderna are now targeting pathogens like Zika, chikungunya and cytomegalovirus, or CMV, which previously considered commercially unviable for vaccine developers. This is because outbreaks have been relatively sporadic, and these viruses mainly affect people in low-income nations who can't afford to pay premium prices for a vaccine. But mRNA technology means that jabs could be produced on a flexible basis, when required, at relatively low cost.
Other scientists suggest that mRNA could even provide a means of developing a universal influenza vaccine, a goal that's long been the Holy Grail for vaccinologists around the world.
"The mRNA technology allows you to pick out bits of the virus that you want to induce immunity to," says Michael Mulqueen, vice president of business development at eTheRNA, a Belgium-based biotech that's developing mRNA-based vaccines for malaria and HIV, as well as various forms of cancer. "This means you can get the immune system primed to the bits of the virus that don't vary so much between strains. So you could actually have a single vaccine that protects against a whole raft of different variants of the same virus, offering more universal coverage."
Before mRNA became synonymous with vaccines, its biggest potential was for cancer treatments. BioNTech, the German biotech company that collaborated with Pfizer to develop the first authorized COVID-19 vaccine, was initially founded to utilize mRNA for personalized cancer treatments, and the company remains interested in cancers ranging from melanoma to breast cancer.
One of the major hurdles in treating cancer has been the fact that tumors can look very different from one person to the next. It's why conventional approaches, such as chemotherapy or radiation, don't work for every patient. But weaponizing mRNA against cancer primes the immune cells with the tumor's specific genetic sequence, training the patient's body to attack their own unique type of cancer.
"It means you're able to think about personalizing cancer treatments down to specific subgroups of patients," says Mulqueen. "For example, eTheRNA are developing a renal cell carcinoma treatment which will be targeted at around 20% of these patients, who have specific tumor types. We're hoping to take that to human trials next year, but the challenge is trying to identify the right patients for the treatment at an early stage."
Repairing Damaged mRNA
While hopes are high that mRNA could usher in new cancer treatments and make CRISPR more accessible, a growing number of companies are also exploring an alternative to gene editing, known as RNA editing.
In genetic disorders, the mRNA in certain cells is impaired due to a rogue gene defect, and so the body ceases to produce a particular vital protein. Instead of permanently deleting the problem gene with CRISPR, the idea behind RNA editing is to inject small pieces of synthetic mRNA to repair the existing mRNA. Scientists think this approach will allow normal protein production to resume.
Over the past few years, this approach has gathered momentum, as some researchers have recognized that it holds certain key advantages over CRISPR. Companies from Belgium to Japan are now looking at RNA editing to treat all kinds of disorders, from Huntingdon's disease, to amyotrophic lateral sclerosis, or ALS, and certain types of cancer.
"With RNA editing, you don't need to make any changes to the DNA," explains Daniel de Boer, CEO of Dutch biotech ProQR, which is looking to treat rare genetic disorders that cause blindness. "Changes to the DNA are permanent, so if something goes wrong, that may not be desirable. With RNA editing, it's a temporary change, so we dose patients with our drugs once or twice a year."
Last month, ProQR reported a landmark case study, in which a patient with a rare form of blindness called Leber congenital amaurosis, which affects the retina at the back of the eye, recovered vision after three months of treatment.
"We have seen that this RNA therapy restores vision in people that were completely blind for a year or so," says de Boer. "They were able to see again, to read again. We think there are a large number of other genetic diseases we could go after with this technology. There are thousands of different mutations that can lead to blindness, and we think this technology can target approximately 25% of them."
Ultimately, there's likely to be a role for both RNA editing and CRISPR, depending on the disease. "I think CRISPR is ideally suited for illnesses where you would like to permanently correct a genetic defect," says Joshua Rosenthal of the Marine Biology Laboratory in Chicago. "Whereas RNA editing could be used to treat things like pain, where you might want to reset a neural circuit temporarily over a shorter period of time."
Much of this research has been accelerated by the COVID-19 pandemic, which has played a major role in bringing mRNA to the forefront of people's minds as a therapeutic.
"The pandemic has really shown that not only are mRNA approaches viable, they could in certain circumstances be vastly superior to more traditional technologies," says Mulqueen. "In the future, I would not be surprised if many of the top pharma products are mRNA derived."
"Making Sense of Science" is a monthly podcast that features interviews with leading medical and scientific experts about the latest developments and the big ethical and societal questions they raise. This episode is hosted by science and biotech journalist Emily Mullin, summer editor of the award-winning science outlet Leaps.org.