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 Friday Five: How to exercise for cancer prevention
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
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Here are the promising studies covered in this week's Friday Five:
- How to exercise for cancer prevention
- A device that brings relief to back pain
- Ingredients for reducing Alzheimer's risk
- Is the world's oldest disease the fountain of youth?
- Scared of crossing bridges? Your phone can help
New approach to brain health is sparking memories
What if a few painless electrical zaps to your brain could help you recall names, perform better on Wordle or even ward off dementia?
This is where neuroscientists are going in efforts to stave off age-related memory loss as well as Alzheimer’s disease. Medications have shown limited effectiveness in reversing or managing loss of brain function so far. But new studies suggest that firing up an aging neural network with electrical or magnetic current might keep brains spry as we age.
Welcome to non-invasive brain stimulation (NIBS). No surgery or anesthesia is required. One day, a jolt in the morning with your own battery-operated kit could replace your wake-up coffee.
Scientists believe brain circuits tend to uncouple as we age. Since brain neurons communicate by exchanging electrical impulses with each other, the breakdown of these links and associations could be what causes the “senior moment”—when you can’t remember the name of the movie you just watched.
In 2019, Boston University researchers led by Robert Reinhart, director of the Cognitive and Clinical Neuroscience Laboratory, showed that memory loss in healthy older adults is likely caused by these disconnected brain networks. When Reinhart and his team stimulated two key areas of the brain with mild electrical current, they were able to bring the brains of older adult subjects back into sync — enough so that their ability to remember small differences between two images matched that of much younger subjects for at least 50 minutes after the testing stopped.
Reinhart wowed the neuroscience community once again this fall. His newer study in Nature Neuroscience presented 150 healthy participants, ages 65 to 88, who were able to recall more words on a given list after 20 minutes of low-intensity electrical stimulation sessions over four consecutive days. This amounted to a 50 to 65 percent boost in their recall.
Even Reinhart was surprised to discover the enhanced performance of his subjects lasted a full month when they were tested again later. Those who benefited most were the participants who were the most forgetful at the start.
An older person participates in Robert Reinhart's research on brain stimulation.
Robert Reinhart
Reinhart’s subjects only suffered normal age-related memory deficits, but NIBS has great potential to help people with cognitive impairment and dementia, too, says Krista Lanctôt, the Bernick Chair of Geriatric Psychopharmacology at Sunnybrook Health Sciences Center in Toronto. Plus, “it is remarkably safe,” she says.
Lanctôt was the senior author on a meta-analysis of brain stimulation studies published last year on people with mild cognitive impairment or later stages of Alzheimer’s disease. The review concluded that magnetic stimulation to the brain significantly improved the research participants’ neuropsychiatric symptoms, such as apathy and depression. The stimulation also enhanced global cognition, which includes memory, attention, executive function and more.
This is the frontier of neuroscience.
The two main forms of NIBS – and many questions surrounding them
There are two types of NIBS. They differ based on whether electrical or magnetic stimulation is used to create the electric field, the type of device that delivers the electrical current and the strength of the current.
Transcranial Current Brain Stimulation (tES) is an umbrella term for a group of techniques using low-wattage electrical currents to manipulate activity in the brain. The current is delivered to the scalp or forehead via electrodes attached to a nylon elastic cap or rubber headband.
Variations include how the current is delivered—in an alternating pattern or in a constant, direct mode, for instance. Tweaking frequency, potency or target brain area can produce different effects as well. Reinhart’s 2022 study demonstrated that low or high frequencies and alternating currents were uniquely tied to either short-term or long-term memory improvements.
Sessions may be 20 minutes per day over the course of several days or two weeks. “[The subject] may feel a tingling, warming, poking or itching sensation,” says Reinhart, which typically goes away within a minute.
The other main approach to NIBS is Transcranial Magnetic Simulation (TMS). It involves the use of an electromagnetic coil that is held or placed against the forehead or scalp to activate nerve cells in the brain through short pulses. The stimulation is stronger than tES but similar to a magnetic resonance imaging (MRI) scan.
The subject may feel a slight knocking or tapping on the head during a 20-to-60-minute session. Scalp discomfort and headaches are reported by some; in very rare cases, a seizure can occur.
No head-to-head trials have been conducted yet to evaluate the differences and effectiveness between electrical and magnetic current stimulation, notes Lanctôt, who is also a professor of psychiatry and pharmacology at the University of Toronto. Although TMS was approved by the FDA in 2008 to treat major depression, both techniques are considered experimental for the purpose of cognitive enhancement.
“One attractive feature of tES is that it’s inexpensive—one-fifth the price of magnetic stimulation,” Reinhart notes.
Don’t confuse either of these procedures with the horrors of electroconvulsive therapy (ECT) in the 1950s and ‘60s. ECT is a more powerful, riskier procedure used only as a last resort in treating severe mental illness today.
Clinical studies on NIBS remain scarce. Standardized parameters and measures for testing have not been developed. The high heterogeneity among the many existing small NIBS studies makes it difficult to draw general conclusions. Few of the studies have been replicated and inconsistencies abound.
Scientists are still lacking so much fundamental knowledge about the brain and how it works, says Reinhart. “We don’t know how information is represented in the brain or how it’s carried forward in time. It’s more complex than physics.”
Lanctôt’s meta-analysis showed improvements in global cognition from delivering the magnetic form of the stimulation to people with Alzheimer’s, and this finding was replicated inan analysis in the Journal of Prevention of Alzheimer’s Disease this fall. Neither meta-analysis found clear evidence that applying the electrical currents, was helpful for Alzheimer’s subjects, but Lanctôt suggests this might be merely because the sample size for tES was smaller compared to the groups that received TMS.
At the same time, London neuroscientist Marco Sandrini, senior lecturer in psychology at the University of Roehampton, critically reviewed a series of studies on the effects of tES on episodic memory. Often declining with age, episodic memory relates to recalling a person’s own experiences from the past. Sandrini’s review concluded that delivering tES to the prefrontal or temporoparietal cortices of the brain might enhance episodic memory in older adults with Alzheimer’s disease and amnesiac mild cognitive impairment (the predementia phase of Alzheimer’s when people start to have symptoms).
Researchers readily tick off studies needed to explore, clarify and validate existing NIBS data. What is the optimal stimulus session frequency, spacing and duration? How intense should the stimulus be and where should it be targeted for what effect? How might genetics or degree of brain impairment affect responsiveness? Would adjunct medication or cognitive training boost positive results? Could administering the stimulus while someone sleeps expedite memory consolidation?
Using MRI or another brain scan along with computational modeling of the current flow, a clinician could create a treatment that is customized to each person’s brain.
While Sandrini’s review reported improvements induced by tES in the recall or recognition of words and images, there is no evidence it will translate into improvements in daily activities. This is another question that will require more research and testing, Sandrini notes.
Scientists are still lacking so much fundamental knowledge about the brain and how it works, says Reinhart. “We don’t know how information is represented in the brain or how it’s carried forward in time. It’s more complex than physics.”
Where the science is headed
Learning how to apply precision medicine to NIBS is the next focus in advancing this technology, says Shankar Tumati, a post-doctoral fellow working with Lanctôt.
There is great variability in each person’s brain anatomy—the thickness of the skull, the brain’s unique folds, the amount of cerebrospinal fluid. All of these structural differences impact how electrical or magnetic stimulation is distributed in the brain and ultimately the effects.
Using MRI or another brain scan along with computational modeling of the current flow, a clinician could create a treatment that is customized to each person’s brain, from where to put the electrodes to determining the exact dose and duration of stimulation needed to achieve lasting results, Sandrini says.
Above all, most neuroscientists say that largescale research studies over long periods of time are necessary to confirm the safety and durability of this therapy for the purpose of boosting memory. Short of that, there can be no FDA approval or medical regulation for this clinical use.
Lanctôt urges people to seek out clinical NIBS trials in their area if they want to see the science advance. “That is how we’ll find the answers,” she says, predicting it will be 5 to 10 years to develop each additional clinical application of NIBS. Ultimately, she predicts that reigning in Alzheimer’s disease and mild cognitive impairment will require a multi-pronged approach that includes lifestyle and medications, too.
Sandrini believes that scientific efforts should focus on preventing or delaying Alzheimer’s. “We need to start intervention earlier—as soon as people start to complain about forgetting things,” he says. “Changes in the brain start 10 years before [there is a problem]. Once Alzheimer’s develops, it is too late.”