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."
The U.S. must fund more biotech innovation – or other countries will catch up faster than you think
The U.S. has approximately 58 percent of the market share in the biotech sector, followed by China with 11 percent. However, this market share is the result of several years of previous research and development (R&D) – it is a present picture of what happened in the past. In the future, this market share will decline unless the federal government makes investments to improve the quality and quantity of U.S. research in biotech.
The effectiveness of current R&D can be evaluated in a variety of ways such as monies invested and the number of patents filed. According to the UNESCO Institute for Statistics, the U.S. spends approximately 2.7 percent of GDP on R&D ($476,459.0M), whereas China spends 2 percent ($346,266.3M). However, investment levels do not necessarily translate into goods that end up contributing to innovation.
Patents are a better indication of innovation. The biotech industry relies on patents to protect their investments, making patenting a key tool in the process of translating scientific discoveries that can ultimately benefit patients. In 2020, China filed 1,497,159 patents, a 6.9 percent increase in growth rate. In contrast, the U.S. filed 597,172, a 3.9 percent decline. When it comes to patents filed, China has approximately 45 percent of the world share compared to 18 percent for the U.S.
So how did we get here? The nature of science in academia allows scientists to specialize by dedicating several years to advance discovery research and develop new inventions that can then be licensed by biotech companies. This makes academic science critical to innovation in the U.S. and abroad.
Academic scientists rely on government and foundation grants to pay for R&D, which includes salaries for faculty, investigators and trainees, as well as monies for infrastructure, support personnel and research supplies. Of particular interest to academic scientists to cover these costs is government support such as Research Project Grants, also known as R01 grants, the oldest grant mechanism from the National Institutes of Health. Unfortunately, this funding mechanism is extremely competitive, as applications have a success rate of only about 20 percent. To maximize the chances of getting funded, investigators tend to limit the innovation of their applications, since a project that seems overambitious is discouraged by grant reviewers.
Considering the difficulty in obtaining funding, the limited number of opportunities for scientists to become independent investigators capable of leading their own scientific projects, and the salaries available to pay for scientists with a doctoral degree, it is not surprising that the U.S. is progressively losing its workforce for innovation.
This approach affects the future success of the R&D enterprise in the U.S. Pursuing less innovative work tends to produce scientific results that are more obvious than groundbreaking, and when a discovery is obvious, it cannot be patented, resulting in fewer inventions that go on to benefit patients. Even though there are governmental funding options available for scientists in academia focused on more groundbreaking and translational projects, those options are less coveted by academic scientists who are trying to obtain tenure and long-term funding to cover salaries and other associated laboratory expenses. Therefore, since only a small percent of projects gets funded, the likelihood of scientists interested in pursuing academic science or even research in general keeps declining over time.
Efforts to raise the number of individuals who pursue a scientific education are paying off. However, the number of job openings for those trainees to carry out independent scientific research once they graduate has proved harder to increase. These limitations are not just in the number of faculty openings to pursue academic science, which are in part related to grant funding, but also the low salary available to pay those scientists after they obtain their doctoral degree, which ranges from $53,000 to $65,000, depending on years of experience.
Thus, considering the difficulty in obtaining funding, the limited number of opportunities for scientists to become independent investigators capable of leading their own scientific projects, and the salaries available to pay for scientists with a doctoral degree, it is not surprising that the U.S. is progressively losing its workforce for innovation, which results in fewer patents filed.
Perhaps instead of encouraging scientists to propose less innovative projects in order to increase their chances of getting grants, the U.S. government should give serious consideration to funding investigators for their potential for success -- or the success they have already achieved in contributing to the advancement of science. Such a funding approach should be tiered depending on career stage or years of experience, considering that 42 years old is the median age at which the first R01 is obtained. This suggests that after finishing their training, scientists spend 10 years before they establish themselves as independent academic investigators capable of having the appropriate funds to train the next generation of scientists who will help the U.S. maintain or even expand its market share in the biotech industry for years to come. Patenting should be given more weight as part of the academic endeavor for promotion purposes, or governmental investment in research funding should be increased to support more than just 20 percent of projects.
Remaining at the forefront of biotech innovation will give us the opportunity to not just generate more jobs, but it will also allow us to attract the brightest scientists from all over the world. This talented workforce will go on to train future U.S. scientists and will improve our standard of living by giving us the opportunity to produce the next generation of therapies intended to improve human health.
This problem cannot rely on just one solution, but what is certain is that unless there are more creative changes in funding approaches for scientists in academia, eventually we may be saying “remember when the U.S. was at the forefront of biotech innovation?”
New gene therapy helps patients with rare disease. One mother wouldn't have it any other way.
Three years ago, Jordan Janz of Consort, Alberta, knew his gene therapy treatment for cystinosis was working when his hair started to darken. Pigmentation or melanin production is just one part of the body damaged by cystinosis.
“When you have cystinosis, you’re either a redhead or a blonde, and you are very pale,” attests Janz, 23, who was diagnosed with the disease just eight months after he was born. “After I got my new stem cells, my hair came back dark, dirty blonde, then it lightened a little bit, but before it was white blonde, almost bleach blonde.”
According to Cystinosis United, about 500 to 600 people have the rare genetic disease in the U.S.; an estimated 20 new cases are diagnosed each year.
Located in Cambridge, Mass., AVROBIO is a gene therapy company that targets cystinosis and other lysosomal storage disorders, in which toxic materials build up in the cells. Janz is one of five patients in AVROBIO’s ongoing Phase 1/2 clinical trial of a gene therapy for cystinosis called AVR-RD-04.
Recently, AVROBIO compiled positive clinical data from this first and only gene therapy trial for the disease. The data show the potential of the therapy to genetically modify the patients’ own hematopoietic stem cells—a certain type of cell that’s capable of developing into all different types of blood cells—to express the functional protein they are deficient in. It stabilizes or reduces the impact of cystinosis on multiple tissues with a single dose.
Medical researchers have found that more than 80 different mutations to a gene called CTNS are responsible for causing cystinosis. The most common mutation results in a deficiency of the protein cystinosin. That protein functions as a transporter that regulates a lot metabolic processes in the cells.
“One of the first things we see in patients clinically is an accumulation of a particular amino acid called cystine, which grows toxic cystine crystals in the cells that cause serious complications,” explains Essra Rihda, chief medical officer for AVROBIO. “That happens in the cells across the tissues and organs of the body, so the disease affects many parts of the body.”
Jordan Janz, 23, meets Stephanie Cherqui, the principal investigator of his gene therapy trial, before the trial started in 2019.
Jordan Janz
According to Rihda, although cystinosis can occur in kids and adults, the most severe form of the disease affects infants and makes up about 95 percent of overall cases. Children typically appear healthy at birth, but around six to 18 months, they start to present for medical attention with failure to thrive.
Additionally, infants with cystinosis often urinate frequently, a sign of polyuria, and they are thirsty all the time, since the disease usually starts in the kidneys. Many develop chronic kidney disease that ultimately progresses to the point where the kidney no longer supports the body’s needs. At that stage, dialysis is required and then a transplant. From there the disease spreads to many other organs, including the eyes, muscles, heart, nervous system, etc.
“The gene for cystinosis is expressed in every single tissue we have, and the accumulation of this toxic buildup alters all of the organs of the patient, so little by little all of the organs start to fail,” says Stephanie Cherqui, principal investigator of Cherqui Lab, which is part of UC San Diego’s Department of Pediatrics.
Since the 1950s, a drug called cysteamine showed some therapeutic effect on cystinosis. It was approved by the FDA in 1994 to prevent damage that may be caused by the buildup of cystine crystals in organs. Prior to FDA approval, Cherqui says, children were dying of the disease before they were ten-years-old or after a kidney transplant. By taking oral cysteamine, they can live from 20 to 50 years longer. But it’s a challenging drug because it has to be taken every 6 or 12 hours, and there are serious gastric side effects such as nausea and diarrhea.
“With all of the complications they develop, the typical patient takes 40 to 60 pills a day around the clock,” Cherqui says. “They literally have a suitcase of medications they have to carry everywhere, and all of those medications don’t stop the progression of the disease, and they still die from it.”
Cherqui has been a proponent of gene therapy to treat children’s disorders since studying cystinosis while earning her doctorate in 2002. Today, her lab focuses on developing stem cell and gene therapy strategies for degenerative, hereditary disorders such as cystinosis that affect multiple systems of the body. “Because cystinosis expresses in every tissue in the body, I decided to use the blood-forming stem cells that we have in our bone marrow,” she explains. “These cells can migrate to anywhere in the body where the person has an injury from the disease.”
AVROBIO’s hematopoietic stem cell gene therapy approach collects stem cells from the patient’s bone marrow. They then genetically modify the stem cells to give the patient a copy of the healthy CTNS gene, which the person either doesn’t have or it’s defective.
The patient first undergoes apheresis, a medical procedure in which their blood is passed through an apparatus that separates out the diseased stem cells, and a process called conditioning is used to help eliminate the damaged cells so they can be replaced by the infusion of the patient’s genetically modified stem cells. Once they become engrafted into the patient’s bone marrow, they reproduce into a lot of daughter cells, and all of those daughter cells contain the CTNS gene. Those cells are able to express the healthy, functional, active protein throughout the body to correct the metabolic problem caused by cystinosis.
“What we’re seeing in the adult patients who have been dosed to date is the consistent and sustained engraftment of our genetically modified cells, 17 to 27 months post-gene therapy, so that’s very encouraging and positive,” says Rihda, the chief medical officer at AVROBIO.
When Janz was 11-years-old, his mother got him enrolled in the trial of a new form of cysteamine that would only need to be taken every 12 hours instead of every six. Two years later, she made sure he was the first person on the list for Cherqui’s current stem cell gene therapy trial.
AVROBIO researchers have also confirmed stabilization or improvement in motor coordination and visual perception in the trial participants, suggesting a potential impact on the neuropathology of the disease. Data from five dosed patients show strong safety and tolerability as well as reduced accumulation of cystine crystals in cells across multiple tissues in the first three patients. None of the five patients need to take oral cysteamine.
Janz’s mother, Barb Kulyk, whom he credits with always making him take his medications and keeping him hydrated, had been following Cherqui’s research since his early childhood. When Janz was 11-years-old, she got him enrolled in the trial of a new form of cysteamine that would only need to be taken every 12 hours instead of every six. When he was 17, the FDA approved that drug. Two years later, his mother made sure he was the first person on the list for Cherqui’s current stem cell gene therapy trial. He received his new stem cells on October 7th, 2019, went home in January 2020, and returned to working full time in February.
Jordan Janz, pictured here with his girlfriend, has a new lease on life, plus a new hair color.
Jordan Janz
He notes that his energy level is significantly better, and his mother has noticed much improvement in him and his daily functioning: He rarely vomits or gets nauseous in the morning, and he has more color in his face as well as his hair. Although he could finish his participation at any time, he recently decided to continue in the clinical trial.
Before the trial, Janz was taking 56 pills daily. He is completely off all of those medications and only takes pills to keep his kidneys working. Because of the damage caused by cystinosis over the course of his life, he’s down to about 20 percent kidney function and will eventually need a transplant.
“Some day, though, thanks to Dr. Cherqui’s team and AVROBIO’s work, when I get a new kidney, cystinosis won’t destroy it,” he concludes.