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Big data, personalized medicine, artificial intelligence. String these three buzzphrases together, and what do you have?
A system that may revolutionize the future of healthcare, by bringing sophisticated health data directly to patients for them to ponder, digest, and act upon—and potentially stop diseases in their tracks.
At Singularity University’s Exponential Medicine conference in San Diego this week, Dr. Ran Balicer, director of the Clalit Research Institute in Israel, painted a futuristic picture of how big data can merge with personalized healthcare into an app-based system in which the patient is in control.
Dr. Ran Balicer at Exponential Medicine
Picture this: instead of going to a physician with your ailments, your doctor calls you with some bad news: “Within six hours, you’re going to have a heart attack. So why don’t you come into the clinic and we can fix that.” Crisis averted.
Following the treatment, you’re at home monitoring your biomarkers, lab test results, and other health information through an app with a clean, beautiful user interface. Within the app, you can observe how various health-influencing life habits—smoking, drinking, insufficient sleep—influence your chance of future cardiovascular disease risks by toggling their levels up or down.
There’s more: you can also set a health goal within the app—for example, stop smoking—which automatically informs your physician. The app will then suggest pharmaceuticals to help you ditch the nicotine and automatically sends the prescription to your local drug store. You’ll also immediately find a list of nearby support groups that can help you reach your health goal.
With this hefty dose of AI, you’re in charge of your health—in fact, probably more so than under current healthcare systems.
Sound fantastical? In fact, this type of preemptive care is already being provided in some countries, including Israel, at a massive scale, said Balicer. By mining datasets with deep learning and other powerful AI tools, we can predict the future—and put it into the hands of patients.
The Israeli Advantage
In order to apply big data approaches to medicine, you first need a giant database.
Israel is ahead of the game in this regard. With decades of electronic health records aggregated within a central warehouse, Israel offers a wealth of health-related data on the scale of millions of people and billions of data points. The data is incredibly multiplex, covering lab tests, drugs, hospital admissions, medical procedures, and more.
One of Balicer’s early successes was an algorithm that predicts diabetes, which allowed the team to notify physicians to target their care. Clalit has also been busy digging into data that predicts winter pneumonia, osteoporosis, and a long list of other preventable diseases.
So far, Balicer’s predictive health system has only been tested on a pilot group of patients, but he is expecting to roll out the platform to all patients in the database in the next few months.
Truly Personalized Medicine
To Balicer, whatever a machine can do better, it should be welcomed to do. AI diagnosticians have already enjoyed plenty of successes—but their collaboration remains mostly with physicians, at a point in time when the patient is already ill.
A particularly powerful use of AI in medicine is to bring insights and trends directly to the patient, such that they can take control over their own health and medical care.
For example, take the problem of tailored drug dosing. Current drug doses are based on average results conducted during clinical trials—the dosing is not tailored for any specific patient’s genetic and health makeup. But what if a doctor had already seen millions of other patients similar to your case, and could generate dosing recommendations more relevant to you based on that particular group of patients?
Such personalized recommendations are beyond the ability of any single human doctor. But with the help of AI, which can quickly process massive datasets to find similarities, doctors may soon be able to prescribe individually-tailored medications.
Tailored treatment doesn’t stop there. Another issue with pharmaceuticals and treatment regimes is that they often come with side effects: potentially health-threatening reactions that may, or may not, happen to you based on your biometrics.
Back in 2017, the New England Journal of Medicine launched the SPRINT Data Analysis Challenge, which urged physicians and data analysts to identify novel clinical findings using shared clinical trial data.
Working with Dr. Noa Dagan at the Clalit Research Institute, Balicer and team developed an algorithm that recommends whether or not a patient receives a particularly intensive treatment regime for hypertension.
Rather than simply looking at one outcome—normalized blood pressure—the algorithm takes into account an individual’s specific characteristics, laying out the treatment’s predicted benefits and harms for a particular patient.
“We built thousands of models for each patient to comprehensively understand the impact of the treatment for the individual; for example, a reduced risk for stroke and cardiovascular-related deaths could be accompanied by an increase in serious renal failure,” said Balicer. “This approach allows a truly personalized balance—allowing patients and their physicians to ultimately decide if the risks of the treatment are worth the benefits.”
This is already personalized medicine at its finest. But Balicer didn’t stop there.
We are not the sum of our biologics and medical stats, he said. A truly personalized approach needs to take a patient’s needs and goals and the sacrifices and tradeoffs they’re willing to make into account, rather than having the physician make decisions for them.
Balicer’s preventative system adds this layer of complexity by giving weights to different outcomes based on patients’ input of their own health goals. Rather than blindly following big data, the system holistically integrates the patient’s opinion to make recommendations.
Balicer’s system is just one example of how AI can truly transform personalized health care. The next big challenge is to work with physicians to further optimize these systems, in a way that doctors can easily integrate them into their workflow and embrace the technology.
“Health systems will not be replaced by algorithms, rest assured,” concluded Balicer, “but health systems that don’t use algorithms will be replaced by those that do.”
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Last year, a novelist went on a road trip across the USA. The trip was an attempt to emulate Jack Kerouac—to go out on the road and find something essential to write about in the experience. There is, however, a key difference between this writer and anyone else talking your ear off in the bar. This writer is just a microphone, a GPS, and a camera hooked up to a laptop and a whole bunch of linear algebra.
People who are optimistic that artificial intelligence and machine learning won’t put us all out of a job say that human ingenuity and creativity will be difficult to imitate. The classic argument is that, just as machines freed us from repetitive manual tasks, machine learning will free us from repetitive intellectual tasks.
This leaves us free to spend more time on the rewarding aspects of our work, pursuing creative hobbies, spending time with loved ones, and generally being human.
In this worldview, creative works like a great novel or symphony, and the emotions they evoke, cannot be reduced to lines of code. Humans retain a dimension of superiority over algorithms.
But is creativity a fundamentally human phenomenon? Or can it be learned by machines?
And if they learn to understand us better than we understand ourselves, could the great AI novel—tailored, of course, to your own predispositions in fiction—be the best you’ll ever read?
Maybe Not a Beach Read
This is the futurist’s view, of course. The reality, as the jury-rigged contraption in Ross Goodwin’s Cadillac for that road trip can attest, is some way off.
“This is very much an imperfect document, a rapid prototyping project. The output isn’t perfect. I don’t think it’s a human novel, or anywhere near it,” Goodwin said of the novel that his machine created. 1 The Road is currently marketed as the first novel written by AI.
Once the neural network has been trained, it can generate any length of text that the author desires, either at random or working from a specific seed word or phrase. Goodwin used the sights and sounds of the road trip to provide these seeds: the novel is written one sentence at a time, based on images, locations, dialogue from the microphone, and even the computer’s own internal clock.
The results are… mixed.
The novel begins suitably enough, quoting the time: “It was nine seventeen in the morning, and the house was heavy.” Descriptions of locations begin according to the Foursquare dataset fed into the algorithm, but rapidly veer off into the weeds, becoming surreal. While experimentation in literature is a wonderful thing, repeatedly quoting longitude and latitude coordinates verbatim is unlikely to win anyone the Booker Prize.
Data In, Art Out?
Neural networks as creative agents have some advantages. They excel at being trained on large datasets, identifying the patterns in those datasets, and producing output that follows those same rules. Music inspired by or written by AI has become a growing subgenre—there’s even a pop album by human-machine collaborators called the Songularity.
A neural network can “listen to” all of Bach and Mozart in hours, and train itself on the works of Shakespeare to produce passable pseudo-Bard. The idea of artificial creativity has become so widespread that there’s even a meme format about forcibly training neural network ‘bots’ on human writing samples, with hilarious consequences—although the best joke was undoubtedly human in origin.
The AI that roamed from New York to New Orleans was an LSTM (long short-term memory) neural net. By default, information contained in individual neurons is preserved, and only small parts can be “forgotten” or “learned” in an individual timestep, rather than neurons being entirely overwritten.
The LSTM architecture performs better than previous recurrent neural networks at tasks such as handwriting and speech recognition. The neural net—and its programmer—looked further in search of literary influences, ingesting 60 million words (360 MB) of raw literature according to Goodwin’s recipe: one third poetry, one third science fiction, and one third “bleak” literature.
In this way, Goodwin has some creative control over the project; the source material influences the machine’s vocabulary and sentence structuring, and hence the tone of the piece.
The Thoughts Beneath the Words
The problem with artificially intelligent novelists is the same problem with conversational artificial intelligence that computer scientists have been trying to solve from Turing’s day. The machines can understand and reproduce complex patterns increasingly better than humans can, but they have no understanding of what these patterns mean.
Goodwin’s neural network spits out sentences one letter at a time, on a tiny printer hooked up to the laptop. Statistical associations such as those tracked by neural nets can form words from letters, and sentences from words, but they know nothing of character or plot.
When talking to a chatbot, the code has no real understanding of what’s been said before, and there is no dataset large enough to train it through all of the billions of possible conversations.
Unless restricted to a predetermined set of options, it loses the thread of the conversation after a reply or two. In a similar way, the creative neural nets have no real grasp of what they’re writing, and no way to produce anything with any overarching coherence or narrative.
Goodwin’s experiment is an attempt to add some coherent backbone to the AI “novel” by repeatedly grounding it with stimuli from the cameras or microphones—the thematic links and narrative provided by the American landscape the neural network drives through.
Goodwin feels that this approach (the car itself moving through the landscape, as if a character) borrows some continuity and coherence from the journey itself. “Coherent prose is the holy grail of natural-language generation—feeling that I had somehow solved a small part of the problem was exhilarating. And I do think it makes a point about language in time that’s unexpected and interesting.”
AI Is Still No Kerouac
A coherent tone and semantic “style” might be enough to produce some vaguely-convincing teenage poetry, as Google did, and experimental fiction that uses neural networks can have intriguing results. But wading through the surreal AI prose of this era, searching for some meaning or motif beyond novelty value, can be a frustrating experience.
Maybe machines can learn the complexities of the human heart and brain, or how to write evocative or entertaining prose. But they’re a long way off, and somehow “more layers!” or a bigger corpus of data doesn’t feel like enough to bridge that gulf.
Real attempts by machines to write fiction have so far been broadly incoherent, but with flashes of poetry—dreamlike, hallucinatory ramblings.
Neural networks might not be capable of writing intricately-plotted works with charm and wit, like Dickens or Dostoevsky, but there’s still an eeriness to trying to decipher the surreal, Finnegans’ Wake mish-mash.
You might see, in the odd line, the flickering ghost of something like consciousness, a deeper understanding. Or you might just see fragments of meaning thrown into a neural network blender, full of hype and fury, obeying rules in an occasionally striking way, but ultimately signifying nothing. In that sense, at least, the RNN’s grappling with metaphor feels like a metaphor for the hype surrounding the latest AI summer as a whole.
Or, as the human author of On The Road put it: “You guys are going somewhere or just going?”
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