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."
Bacterial antibiotic resistance has been a concern in the medical field for several years. Now a new, similar threat is arising: drug-resistant fungal infections. The Centers for Disease Control and Prevention considers antifungal and antimicrobial resistance to be among the world’s greatest public health challenges.
One particular type of fungal infection caused by Candida auris is escalating rapidly throughout the world. And to make matters worse, C. auris is becoming increasingly resistant to current antifungal medications, which means that if you develop a C. auris infection, the drugs your doctor prescribes may not work. “We’re effectively out of medicines,” says Thomas Walsh, founding director of the Center for Innovative Therapeutics and Diagnostics, a translational research center dedicated to solving the antimicrobial resistance problem. Walsh spoke about the challenges at a Demy-Colton Virtual Salon, one in a series of interactive discussions among life science thought leaders.
Although C. auris typically doesn’t sicken healthy people, it afflicts immunocompromised hospital patients and may cause severe infections that can lead to sepsis, a life-threatening condition in which the overwhelmed immune system begins to attack the body’s own organs. Between 30 and 60 percent of patients who contract a C. auris infection die from it, according to the CDC. People who are undergoing stem cell transplants, have catheters or have taken antifungal or antibiotic medicines are at highest risk. “We’re coming to a perfect storm of increasing resistance rates, increasing numbers of immunosuppressed patients worldwide and a bug that is adapting to higher temperatures as the climate changes,” says Prabhavathi Fernandes, chair of the National BioDefense Science Board.
Most Candida species aren’t well-adapted to our body temperatures so they aren’t a threat. C. auris, however, thrives at human body temperatures.
Although medical professionals aren’t concerned at this point about C. auris evolving to affect healthy people, they worry that its presence in hospitals can turn routine surgeries into life-threatening calamities. “It’s coming,” says Fernandes. “It’s just a matter of time.”
An emerging global threat
“Fungi are found in the environment,” explains Fernandes, so Candida spores can easily wind up on people’s skin. In hospitals, they can be transferred from contact with healthcare workers or contaminated surfaces. Most Candida species aren’t well-adapted to our body temperatures so they aren’t a threat. C. auris, however, thrives at human body temperatures. It can enter the body during medical treatments that break the skin—and cause an infection. Overall, fungal infections cost some $48 billion in the U.S. each year. And infection rates are increasing because, in an ironic twist, advanced medical therapies are enabling severely ill patients to live longer and, therefore, be exposed to this pathogen.
The first-ever case of a C. auris infection was reported in Japan in 2009, although an analysis of Candida samples dated the earliest strain to a 1996 sample from South Korea. Since then, five separate varieties – called clades, which are similar to strains among bacteria – developed independently in different geographies: South Asia, East Asia, South Africa, South America and, recently, Iran. So far, C. auris infections have been reported in 35 countries.
In the U.S., the first infection was reported in 2016, and the CDC started tracking it nationally two years later. During that time, 5,654 cases have been reported to the CDC, which only tracks U.S. data.
What’s more notable than the number of cases is their rate of increase. In 2016, new cases increased by 175 percent and, on average, they have approximately doubled every year. From 2016 through 2022, the number of infections jumped from 63 to 2,377, a roughly 37-fold increase.
“This reminds me of what we saw with epidemics from 2013 through 2020… with Ebola, Zika and the COVID-19 pandemic,” says Robin Robinson, CEO of Spriovas and founding director of the Biomedical Advanced Research and Development Authority (BARDA), which is part of the U.S. Department of Health and Human Services. These epidemics started with a hockey stick trajectory, Robinson says—a gradual growth leading to a sharp spike, just like the shape of a hockey stick.
Another challenge is that right now medics don’t have rapid diagnostic tests for fungal infections. Currently, patients are often misdiagnosed because C. auris resembles several other easily treated fungi. Or they are diagnosed long after the infection begins and is harder to treat.
The problem is that existing diagnostics tests can only identify C. auris once it reaches the bloodstream. Yet, because this pathogen infects bodily tissues first, it should be possible to catch it much earlier before it becomes life-threatening. “We have to diagnose it before it reaches the bloodstream,” Walsh says.
The most alarming fact is that some Candida infections no longer respond to standard therapeutics.
“We need to focus on rapid diagnostic tests that do not rely on a positive blood culture,” says John Sperzel, president and CEO of T2 Biosystems, a company specializing in diagnostics solutions. Blood cultures typically take two to three days for the concentration of Candida to become large enough to detect. The company’s novel test detects about 90 percent of Candida species within three to five hours—thanks to its ability to spot minute quantities of the pathogen in blood samples instead of waiting for them to incubate and proliferate.
Unlike other Candida species C. auris thrives at human body temperatures
Adobe Stock
Tackling the resistance challenge
The most alarming fact is that some Candida infections no longer respond to standard therapeutics. The number of cases that stopped responding to echinocandin, the first-line therapy for most Candida infections, tripled in 2020, according to a study by the CDC.
Now, each of the first four clades shows varying levels of resistance to all three commonly prescribed classes of antifungal medications, such as azoles, echinocandins, and polyenes. For example, 97 percent of infections from C. auris Clade I are resistant to fluconazole, 54 percent to voriconazole and 30 percent of amphotericin. Nearly half are resistant to multiple antifungal drugs. Even with Clade II fungi, which has the least resistance of all the clades, 11 to 14 percent have become resistant to fluconazole.
Anti-fungal therapies typically target specific chemical compounds present on fungi’s cell membranes, but not on human cells—otherwise the medicine would cause damage to our own tissues. Fluconazole and other azole antifungals target a compound called ergosterol, preventing the fungal cells from replicating. Over the years, however, C. auris evolved to resist it, so existing fungal medications don’t work as well anymore.
A newer class of drugs called echinocandins targets a different part of the fungal cell. “The echinocandins – like caspofungin – inhibit (a part of the fungi) involved in making glucan, which is an essential component of the fungal cell wall and is not found in human cells,” Fernandes says. New antifungal treatments are needed, she adds, but there are only a few magic bullets that will hit just the fungus and not the human cells.
Research to fight infections also has been challenged by a lack of government support. That is changing now that BARDA is requesting proposals to develop novel antifungals. “The scope includes C. auris, as well as antifungals following a radiological/nuclear emergency, says BARDA spokesperson Elleen Kane.
The remaining challenge is the number of patients available to participate in clinical trials. Large numbers are needed, but the available patients are quite sick and often die before trials can be completed. Consequently, few biopharmaceutical companies are developing new treatments for C. auris.
ClinicalTrials.gov reports only two drugs in development for invasive C. auris infections—those than can spread throughout the body rather than localize in one particular area, like throat or vaginal infections: ibrexafungerp by Scynexis, Inc., fosmanogepix, by Pfizer.
Scynexis’ ibrexafungerp appears active against C. auris and other emerging, drug-resistant pathogens. The FDA recently approved it as a therapy for vaginal yeast infections and it is undergoing Phase III clinical trials against invasive candidiasis in an attempt to keep the infection from spreading.
“Ibreafungerp is structurally different from other echinocandins,” Fernandes says, because it targets a different part of the fungus. “We’re lucky it has activity against C. auris.”
Pfizer’s fosmanogepix is in Phase II clinical trials for patients with invasive fungal infections caused by multiple Candida species. Results are showing significantly better survival rates for people taking fosmanogepix.
Although C. auris does pose a serious threat to healthcare worldwide, scientists try to stay optimistic—because they recognized the problem early enough, they might have solutions in place before the perfect storm hits. “There is a bit of hope,” says Robinson. “BARDA has finally been able to fund the development of new antifungal agents and, hopefully, this year we can get several new classes of antifungals into development.”
New elevators could lift up our access to space
Story by Big Think
When people first started exploring space in the 1960s, it cost upwards of $80,000 (adjusted for inflation) to put a single pound of payload into low-Earth orbit.
A major reason for this high cost was the need to build a new, expensive rocket for every launch. That really started to change when SpaceX began making cheap, reusable rockets, and today, the company is ferrying customer payloads to LEO at a price of just $1,300 per pound.
This is making space accessible to scientists, startups, and tourists who never could have afforded it previously, but the cheapest way to reach orbit might not be a rocket at all — it could be an elevator.
The space elevator
The seeds for a space elevator were first planted by Russian scientist Konstantin Tsiolkovsky in 1895, who, after visiting the 1,000-foot (305 m) Eiffel Tower, published a paper theorizing about the construction of a structure 22,000 miles (35,400 km) high.
This would provide access to geostationary orbit, an altitude where objects appear to remain fixed above Earth’s surface, but Tsiolkovsky conceded that no material could support the weight of such a tower.
We could then send electrically powered “climber” vehicles up and down the tether to deliver payloads to any Earth orbit.
In 1959, soon after Sputnik, Russian engineer Yuri N. Artsutanov proposed a way around this issue: instead of building a space elevator from the ground up, start at the top. More specifically, he suggested placing a satellite in geostationary orbit and dropping a tether from it down to Earth’s equator. As the tether descended, the satellite would ascend. Once attached to Earth’s surface, the tether would be kept taut, thanks to a combination of gravitational and centrifugal forces.
We could then send electrically powered “climber” vehicles up and down the tether to deliver payloads to any Earth orbit. According to physicist Bradley Edwards, who researched the concept for NASA about 20 years ago, it’d cost $10 billion and take 15 years to build a space elevator, but once operational, the cost of sending a payload to any Earth orbit could be as low as $100 per pound.
“Once you reduce the cost to almost a Fed-Ex kind of level, it opens the doors to lots of people, lots of countries, and lots of companies to get involved in space,” Edwards told Space.com in 2005.
In addition to the economic advantages, a space elevator would also be cleaner than using rockets — there’d be no burning of fuel, no harmful greenhouse emissions — and the new transport system wouldn’t contribute to the problem of space junk to the same degree that expendable rockets do.
So, why don’t we have one yet?
Tether troubles
Edwards wrote in his report for NASA that all of the technology needed to build a space elevator already existed except the material needed to build the tether, which needs to be light but also strong enough to withstand all the huge forces acting upon it.
The good news, according to the report, was that the perfect material — ultra-strong, ultra-tiny “nanotubes” of carbon — would be available in just two years.
“[S]teel is not strong enough, neither is Kevlar, carbon fiber, spider silk, or any other material other than carbon nanotubes,” wrote Edwards. “Fortunately for us, carbon nanotube research is extremely hot right now, and it is progressing quickly to commercial production.”Unfortunately, he misjudged how hard it would be to synthesize carbon nanotubes — to date, no one has been able to grow one longer than 21 inches (53 cm).
Further research into the material revealed that it tends to fray under extreme stress, too, meaning even if we could manufacture carbon nanotubes at the lengths needed, they’d be at risk of snapping, not only destroying the space elevator, but threatening lives on Earth.
Looking ahead
Carbon nanotubes might have been the early frontrunner as the tether material for space elevators, but there are other options, including graphene, an essentially two-dimensional form of carbon that is already easier to scale up than nanotubes (though still not easy).
Contrary to Edwards’ report, Johns Hopkins University researchers Sean Sun and Dan Popescu say Kevlar fibers could work — we would just need to constantly repair the tether, the same way the human body constantly repairs its tendons.
“Using sensors and artificially intelligent software, it would be possible to model the whole tether mathematically so as to predict when, where, and how the fibers would break,” the researchers wrote in Aeon in 2018.
“When they did, speedy robotic climbers patrolling up and down the tether would replace them, adjusting the rate of maintenance and repair as needed — mimicking the sensitivity of biological processes,” they continued.Astronomers from the University of Cambridge and Columbia University also think Kevlar could work for a space elevator — if we built it from the moon, rather than Earth.
They call their concept the Spaceline, and the idea is that a tether attached to the moon’s surface could extend toward Earth’s geostationary orbit, held taut by the pull of our planet’s gravity. We could then use rockets to deliver payloads — and potentially people — to solar-powered climber robots positioned at the end of this 200,000+ mile long tether. The bots could then travel up the line to the moon’s surface.
This wouldn’t eliminate the need for rockets to get into Earth’s orbit, but it would be a cheaper way to get to the moon. The forces acting on a lunar space elevator wouldn’t be as strong as one extending from Earth’s surface, either, according to the researchers, opening up more options for tether materials.
“[T]he necessary strength of the material is much lower than an Earth-based elevator — and thus it could be built from fibers that are already mass-produced … and relatively affordable,” they wrote in a paper shared on the preprint server arXiv.
After riding up the Earth-based space elevator, a capsule would fly to a space station attached to the tether of the moon-based one.
Electrically powered climber capsules could go up down the tether to deliver payloads to any Earth orbit.
Adobe Stock
Some Chinese researchers, meanwhile, aren’t giving up on the idea of using carbon nanotubes for a space elevator — in 2018, a team from Tsinghua University revealed that they’d developed nanotubes that they say are strong enough for a tether.
The researchers are still working on the issue of scaling up production, but in 2021, state-owned news outlet Xinhua released a video depicting an in-development concept, called “Sky Ladder,” that would consist of space elevators above Earth and the moon.
After riding up the Earth-based space elevator, a capsule would fly to a space station attached to the tether of the moon-based one. If the project could be pulled off — a huge if — China predicts Sky Ladder could cut the cost of sending people and goods to the moon by 96 percent.
The bottom line
In the 120 years since Tsiolkovsky looked at the Eiffel Tower and thought way bigger, tremendous progress has been made developing materials with the properties needed for a space elevator. At this point, it seems likely we could one day have a material that can be manufactured at the scale needed for a tether — but by the time that happens, the need for a space elevator may have evaporated.
Several aerospace companies are making progress with their own reusable rockets, and as those join the market with SpaceX, competition could cause launch prices to fall further.
California startup SpinLaunch, meanwhile, is developing a massive centrifuge to fling payloads into space, where much smaller rockets can propel them into orbit. If the company succeeds (another one of those big ifs), it says the system would slash the amount of fuel needed to reach orbit by 70 percent.
Even if SpinLaunch doesn’t get off the ground, several groups are developing environmentally friendly rocket fuels that produce far fewer (or no) harmful emissions. More work is needed to efficiently scale up their production, but overcoming that hurdle will likely be far easier than building a 22,000-mile (35,400-km) elevator to space.