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."
Blood Test Can Detect Lymphoma Cells Before a Tumor Grows Back
When David M. Kurtz was doing his clinical fellowship at Stanford University Medical Center in 2009, specializing in lymphoma treatments, he found himself grappling with a question no one could answer. A typical regimen for these blood cancers prescribed six cycles of chemotherapy, but no one knew why. "The number seemed to be drawn out of a hat," Kurtz says. Some patients felt much better after just two doses, but had to endure the toxic effects of the entire course. For some elderly patients, the side effects of chemo are so harsh, they alone can kill. Others appeared to be cancer-free on the CT scans after the requisite six but then succumbed to it months later.
"Anecdotally, one patient decided to stop therapy after one dose because he felt it was so toxic that he opted for hospice instead," says Kurtz, now an oncologist at the center. "Five years down the road, he was alive and well. For him, just one dose was enough." Others would return for their one-year check up and find that their tumors grew back. Kurtz felt that while CT scans and MRIs were powerful tools, they weren't perfect ones. They couldn't tell him if there were any cancer cells left, stealthily waiting to germinate again. The scans only showed the tumor once it was back.
Blood cancers claim about 68,000 people a year, with a new diagnosis made about every three minutes, according to the Leukemia Research Foundation. For patients with B-cell lymphoma, which Kurtz focuses on, the survival chances are better than for some others. About 60 percent are cured, but the remaining 40 percent will relapse—possibly because they will have a negative CT scan, but still harbor malignant cells. "You can't see this on imaging," says Michael Green, who also treats blood cancers at University of Texas MD Anderson Medical Center.
The new blood test is sensitive enough to spot one cancerous perpetrator amongst one million other DNA molecules.
Kurtz wanted a better diagnostic tool, so he started working on a blood test that could capture the circulating tumor DNA or ctDNA. For that, he needed to identify the specific mutations typical for B-cell lymphomas. Working together with another fellow PhD student Jake Chabon, Kurtz finally zeroed-in on the tumor's genetic "appearance" in 2017—a pair of specific mutations sitting in close proximity to each other—a rare and telling sign. The human genome contains about 3 billion base pairs of nucleotides—molecules that compose genes—and in case of the B-cell lymphoma cells these two mutations were only a few base pairs apart. "That was the moment when the light bulb went on," Kurtz says.
The duo formed a company named Foresight Diagnostics, focusing on taking the blood test to the clinic. But knowing the tumor's mutational signature was only half the process. The other was fishing the tumor's DNA out of patients' bloodstream that contains millions of other DNA molecules, explains Chabon, now Foresight's CEO. It would be like looking for an escaped criminal in a large crowd. Kurtz and Chabon solved the problem by taking the tumor's "mug shot" first. Doctors would take the biopsy pre-treatment and sequence the tumor, as if taking the criminal's photo. After treatments, they would match the "mug shot" to all DNA molecules derived from the patient's blood sample to see if any molecular criminals managed to escape the chemo.
Foresight isn't the only company working on blood-based tumor detection tests, which are dubbed liquid biopsies—other companies such as Natera or ArcherDx developed their own. But in a recent study, the Foresight team showed that their method is significantly more sensitive in "fishing out" the cancer molecules than existing tests. Chabon says that this test can detect circulating tumor DNA in concentrations that are nearly 100 times lower than other methods. Put another way, it's sensitive enough to spot one cancerous perpetrator amongst one million other DNA molecules.
They also aim to extend their test to detect other malignancies such as lung, breast or colorectal cancers.
"It increases the sensitivity of detection and really catches most patients who are going to progress," says Green, the University of Texas oncologist who wasn't involved in the study, but is familiar with the method. It would also allow monitoring patients during treatment and making better-informed decisions about which therapy regimens would be most effective. "It's a minimally invasive test," Green says, and "it gives you a very high confidence about what's going on."
Having shown that the test works well, Kurtz and Chabon are planning a new trial in which oncologists would rely on their method to decide when to stop or continue chemo. They also aim to extend their test to detect other malignancies such as lung, breast or colorectal cancers. The latest genome sequencing technologies have sequenced and catalogued over 2,500 different tumor specimens and the Foresight team is analyzing this data, says Chabon, which gives the team the opportunity to create more molecular "mug shots."
The team hopes that that their blood cancer test will become available to patients within about five years, making doctors' job easier, and not only at the biological level. "When I tell patients, "good news, your cancer is in remission', they ask me, 'does it mean I'm cured?'" Kurtz says. "Right now I can't answer this question because I don't know—but I would like to." His company's test, he hopes, will enable him to reply with certainty. He'd very much like to have the power of that foresight.
This article is republished from our archives to coincide with Blood Cancer Awareness Month, which highlights progress in cancer diagnostics and treatment.
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
The future of non-hormonal birth control: Antibodies can stop sperm in their tracks
Unwanted pregnancy can now be added to the list of preventions that antibodies may be fighting in the near future. For decades, really since the 1980s, engineered monoclonal antibodies have been knocking out invading germs — preventing everything from cancer to COVID. Sperm, which have some of the same properties as germs, may be next.
Not only is there an unmet need on the market for alternatives to hormonal contraceptives, the genesis for the original research was personal for the then 22-year-old scientist who led it. Her findings were used to launch a company that could, within the decade, bring a new kind of contraceptive to the marketplace.
The genesis
It’s Suruchi Shrestha’s research — published in Science Translational Medicine in August 2021 and conducted as part of her dissertation while she was a graduate student at the University of North Carolina at Chapel Hill — that could change the future of contraception for many women worldwide. According to a Guttmacher Institute report, in the U.S. alone, there were 46 million sexually active women of reproductive age (15–49) who did not want to get pregnant in 2018. With the overturning of Roe v. Wade last year, Shrestha’s research could, indeed, be life changing for millions of American women and their families.
Now a scientist with NextVivo, Shrestha is not directly involved in the development of the contraceptive that is based on her research. But, back in 2016 when she was going through her own problems with hormonal contraceptives, she “was very personally invested” in her research project, Shrestha says. She was coping with a long list of negative effects from an implanted hormonal IUD. According to the Mayo Clinic, those can include severe pelvic pain, headaches, acute acne, breast tenderness, irregular bleeding and mood swings. After a year, she had the IUD removed, but it took another full year before all the side effects finally subsided; she also watched her sister suffer the “same tribulations” after trying a hormonal IUD, she says.
For contraceptive use either daily or monthly, Shrestha says, “You want the antibody to be very potent and also cheap.” That was her goal when she launched her study.
Shrestha unshelved antibody research that had been sitting idle for decades. It was in the late 80s that scientists in Japan first tried to develop anti-sperm antibodies for contraceptive use. But, 35 years ago, “Antibody production had not been streamlined as it is now, so antibodies were very expensive,” Shrestha explains. So, they shifted away from birth control, opting to focus on developing antibodies for vaccines.
Over the course of the last three decades, different teams of researchers have been working to make the antibody more effective, bringing the cost down, though it’s still expensive, according to Shrestha. For contraceptive use either daily or monthly, she says, “You want the antibody to be very potent and also cheap.” That was her goal when she launched her study.
The problem
The problem with contraceptives for women, Shrestha says, is that all but a few of them are hormone-based or have other negative side effects. In fact, some studies and reports show that millions of women risk unintended pregnancy because of medical contraindications with hormone-based contraceptives or to avoid the risks and side effects. While there are about a dozen contraceptive choices for women, there are two for men: the condom, considered 98% effective if used correctly, and vasectomy, 99% effective. Neither of these choices are hormone-based.
On the non-hormonal side for women, there is the diaphragm which is considered only 87 percent effective. It works better with the addition of spermicides — Nonoxynol-9, or N-9 — however, they are detergents; they not only kill the sperm, they also erode the vaginal epithelium. And, there’s the non-hormonal IUD which is 99% effective. However, the IUD needs to be inserted by a medical professional, and it has a number of negative side effects, including painful cramping at a higher frequency and extremely heavy or “abnormal” and unpredictable menstrual flows.
The hormonal version of the IUD, also considered 99% effective, is the one Shrestha used which caused her two years of pain. Of course, there’s the pill, which needs to be taken daily, and the birth control ring which is worn 24/7. Both cause side effects similar to the other hormonal contraceptives on the market. The ring is considered 93% effective mostly because of user error; the pill is considered 99% effective if taken correctly.
“That’s where we saw this opening or gap for women. We want a safe, non-hormonal contraceptive,” Shrestha says. Compounding the lack of good choices, is poor access to quality sex education and family planning information, according to the non-profit Urban Institute. A focus group survey suggested that the sex education women received “often lacked substance, leaving them feeling unprepared to make smart decisions about their sexual health and safety,” wrote the authors of the Urban Institute report. In fact, nearly half (45%, or 2.8 million) of the pregnancies that occur each year in the US are unintended, reports the Guttmacher Institute. Globally the numbers are similar. According to a new report by the United Nations, each year there are 121 million unintended pregnancies, worldwide.
The science
The early work on antibodies as a contraceptive had been inspired by women with infertility. It turns out that 9 to 12 percent of women who are treated for infertility have antibodies that develop naturally and work against sperm. Shrestha was encouraged that the antibodies were specific to the target — sperm — and therefore “very safe to use in women.” She aimed to make the antibodies more stable, more effective and less expensive so they could be more easily manufactured.
Since antibodies tend to stick to things that you tell them to stick to, the idea was, basically, to engineer antibodies to stick to sperm so they would stop swimming. Shrestha and her colleagues took the binding arm of an antibody that they’d isolated from an infertile woman. Then, targeting a unique surface antigen present on human sperm, they engineered a panel of antibodies with as many as six to 10 binding arms — “almost like tongs with prongs on the tongs, that bind the sperm,” explains Shrestha. “We decided to add those grabbers on top of it, behind it. So it went from having two prongs to almost 10. And the whole goal was to have so many arms binding the sperm that it clumps it” into a “dollop,” explains Shrestha, who earned a patent on her research.
Suruchi Shrestha works in the lab with a colleague. In 2016, her research on antibodies for birth control was inspired by her own experience with side effects from an implanted hormonal IUD.
UNC - Chapel Hill
The sperm stays right where it met the antibody, never reaching the egg for fertilization. Eventually, and naturally, “Our vaginal system will just flush it out,” Shrestha explains.
“She showed in her early studies that [she] definitely got the sperm immotile, so they didn't move. And that was a really promising start,” says Jasmine Edelstein, a scientist with an expertise in antibody engineering who was not involved in this research. Shrestha’s team at UNC reproduced the effect in the sheep, notes Edelstein, who works at the startup Be Biopharma. In fact, Shrestha’s anti-sperm antibodies that caused the sperm to agglutinate, or clump together, were 99.9% effective when delivered topically to the sheep’s reproductive tracts.
The future
Going forward, Shrestha thinks the ideal approach would be delivering the antibodies through a vaginal ring. “We want to use it at the source of the spark,” Shrestha says, as opposed to less direct methods, such as taking a pill. The ring would dissolve after one month, she explains, “and then you get another one.”
Engineered to have a long shelf life, the anti-sperm antibody ring could be purchased without a prescription, and women could insert it themselves, without a doctor. “That's our hope, so that it is accessible,” Shrestha says. “Anybody can just go and grab it and not worry about pregnancy or unintended pregnancy.”
Her patented research has been licensed by several biotech companies for clinical trials. A number of Shrestha’s co-authors, including her lab advisor, Sam Lai, have launched a company, Mucommune, to continue developing the contraceptives based on these antibodies.
And, results from a small clinical trial run by researchers at Boston University Chobanian & Avedisian School of Medicine show that a dissolvable vaginal film with antibodies was safe when tested on healthy women of reproductive age. That same group of researchers last year received a $7.2 million grant from the National Institute of Health for further research on monoclonal antibody-based contraceptives, which have also been shown to block transmission of viruses, like HIV.
“As the costs come down, this becomes a more realistic option potentially for women,” says Edelstein. “The impact could be tremendous.”
This article was first published by Leaps.org in December, 2022. It has been lightly edited with updates for timeliness.