Your Digital Avatar May One Day Get Sick Before You Do
Artificial neurons in a concept of artificial intelligence.
Artificial intelligence is everywhere, just not in the way you think it is.
These networks, loosely designed after the human brain, are interconnected computers that have the ability to "learn."
"There's the perception of AI in the glossy magazines," says Anders Kofod-Petersen, a professor of Artificial Intelligence at the Norwegian University of Science and Technology. "That's the sci-fi version. It resembles the small guy in the movie AI. It might be benevolent or it might be evil, but it's generally intelligent and conscious."
"And this is, of course, as far from the truth as you can possibly get."
What Exactly Is Artificial Intelligence, Anyway?
Let's start with how you got to this piece. You likely came to it through social media. Your Facebook account, Twitter feed, or perhaps a Google search. AI influences all of those things, machine learning helping to run the algorithms that decide what you see, when, and where. AI isn't the little humanoid figure; it's the system that controls the figure.
"AI is being confused with robotics," Eleonore Pauwels, Director of the Anticipatory Intelligence Lab with the Science and Technology Innovation Program at the Wilson Center, says. "What AI is right now is a data optimization system, a very powerful data optimization system."
The revolution in recent years hasn't come from the method scientists and other researchers use. The general ideas and philosophies have been around since the late 1960s. Instead, the big change has been the dramatic increase in computing power, primarily due to the development of neural networks. These networks, loosely designed after the human brain, are interconnected computers that have the ability to "learn." An AI, for example, can be taught to spot a picture of a cat by looking at hundreds of thousands of pictures that have been labeled "cat" and "learning" what a cat looks like. Or an AI can beat a human at Go, an achievement that just five years ago Kofod-Petersen thought wouldn't be accomplished for decades.
"It's very difficult to argue that something is intelligent if it can't learn, and these algorithms are getting pretty good at learning stuff. What they are not good at is learning how to learn."
Medicine is the field where this expertise in perception tasks might have the most influence. It's already having an impact as iPhones use AI to detect cancer, Apple watches alert the wearer to a heart problem, AI spots tuberculosis and the spread of breast cancer with a higher accuracy than human doctors, and more. Every few months, another study demonstrates more possibility. (The New Yorker published an article about medicine and AI last year, so you know it's a serious topic.)
But this is only the beginning. "I personally think genomics and precision medicine is where AI is going to be the biggest game-changer," Pauwels says. "It's going to completely change how we think about health, our genomes, and how we think about our relationship between our genotype and phenotype."
The Fundamental Breakthrough That Must Be Solved
To get there, however, researchers will need to make another breakthrough, and there's debate about how long that will take. Kofod-Petersen explains: "If we want to move from this narrow intelligence to this broader intelligence, that's a very difficult problem. It basically boils down to that we haven't got a clue about what intelligence actually is. We don't know what intelligence means in a biological sense. We think we might recognize it but we're not completely sure. There isn't a working definition. We kind of agree with the biologists that learning is an aspect of it. It's very difficult to argue that something is intelligent if it can't learn, and these algorithms are getting pretty good at learning stuff. What they are not good at is learning how to learn. They can learn specific tasks but we haven't approached how to teach them to learn to learn."
In other words, current AI is very, very good at identifying that a picture of a cat is, in fact, a cat – and getting better at doing so at an incredibly rapid pace – but the system only knows what a "cat" is because that's what a programmer told it a furry thing with whiskers and two pointy ears is called. If the programmer instead decided to label the training images as "dogs," the AI wouldn't say "no, that's a cat." Instead, it would simply call a furry thing with whiskers and two pointy ears a dog. AI systems lack the explicit inference that humans do effortlessly, almost without thinking.
Pauwels believes that the next step is for AI to transition from supervised to unsupervised learning. The latter means that the AI isn't answering questions that a programmer asks it ("Is this a cat?"). Instead, it's almost like it's looking at the data it has, coming up with its own questions and hypothesis, and answering them or putting them to the test. Combining this ability with the frankly insane processing power of the computer system could result in game-changing discoveries.
In the not-too-distant future, a doctor could run diagnostics on a digital avatar, watching which medical conditions present themselves before the person gets sick in real life.
One company in China plans to develop a way to create a digital avatar of an individual person, then simulate that person's health and medical information into the future. In the not-too-distant future, a doctor could run diagnostics on a digital avatar, watching which medical conditions presented themselves – cancer or a heart condition or anything, really – and help the real-life version prevent those conditions from beginning or treating them before they became a life-threatening issue.
That, obviously, would be an incredibly powerful technology, and it's just one of the many possibilities that unsupervised AI presents. It's also terrifying in the potential for misuse. Even the term "unsupervised AI" brings to mind a dystopian landscape where AI takes over and enslaves humanity. (Pick your favorite movie. There are dozens.) This is a concern, something for developers, programmers, and scientists to consider as they build the systems of the future.
The Ethical Problem That Deserves More Attention
But the more immediate concern about AI is much more mundane. We think of AI as an unbiased system. That's incorrect. Algorithms, after all, are designed by someone or a team, and those people have explicit or implicit biases. Intentionally, or more likely not, they introduce these biases into the very code that forms the basis for the AI. Current systems have a bias against people of color. Facebook tried to rectify the situation and failed. These are two small examples of a larger, potentially systemic problem.
It's vital and necessary for the people developing AI today to be aware of these issues. And, yes, avoid sending us to the brink of a James Cameron movie. But AI is too powerful a tool to ignore. Today, it's identifying cats and on the verge of detecting cancer. In not too many tomorrows, it will be on the forefront of medical innovation. If we are careful, aware, and smart, it will help simulate results, create designer drugs, and revolutionize individualize medicine. "AI is the only way to get there," Pauwels says.
Breakthrough therapies are breaking patients' banks. Key changes could improve access, experts say.
Single-treatment therapies are revolutionizing medicine. But insurers and patients wonder whether they can afford treatment and, if they can, whether the high costs are worthwhile.
CSL Behring’s new gene therapy for hemophilia, Hemgenix, costs $3.5 million for one treatment, but helps the body create substances that allow blood to clot. It appears to be a cure, eliminating the need for other treatments for many years at least.
Likewise, Novartis’s Kymriah mobilizes the body’s immune system to fight B-cell lymphoma, but at a cost $475,000. For patients who respond, it seems to offer years of life without the cancer progressing.
These single-treatment therapies are at the forefront of a new, bold era of medicine. Unfortunately, they also come with new, bold prices that leave insurers and patients wondering whether they can afford treatment and, if they can, whether the high costs are worthwhile.
“Most pharmaceutical leaders are there to improve and save people’s lives,” says Jeremy Levin, chairman and CEO of Ovid Therapeutics, and immediate past chairman of the Biotechnology Innovation Organization. If the therapeutics they develop are too expensive for payers to authorize, patients aren’t helped.
“The right to receive care and the right of pharmaceuticals developers to profit should never be at odds,” Levin stresses. And yet, sometimes they are.
Leigh Turner, executive director of the bioethics program, University of California, Irvine, notes this same tension between drug developers that are “seeking to maximize profits by charging as much as the market will bear for cell and gene therapy products and other medical interventions, and payers trying to control costs while also attempting to provide access to medical products with promising safety and efficacy profiles.”
Why Payers Balk
Health insurers can become skittish around extremely high prices, yet these therapies often accompany significant overall savings. For perspective, the estimated annual treatment cost for hemophilia exceeds $300,000. With Hemgenix, payers would break even after about 12 years.
But, in 12 years, will the patient still have that insurer? Therein lies the rub. U.S. payers, are used to a “pay-as-you-go” model, in which the lifetime costs of therapies typically are shared by multiple payers over many years, as patients change jobs. Single treatment therapeutics eliminate that cost-sharing ability.
"As long as formularies are based on profits to middlemen…Americans’ healthcare costs will continue to skyrocket,” says Patricia Goldsmith, the CEO of CancerCare.
“There is a phenomenally complex, bureaucratic reimbursement system that has grown, layer upon layer, during several decades,” Levin says. As medicine has innovated, payment systems haven’t kept up.
Therefore, biopharma companies begin working with insurance companies and their pharmacy benefit managers (PBMs), which act on an insurer’s behalf to decide which drugs to cover and by how much, early in the drug approval process. Their goal is to make sophisticated new drugs available while still earning a return on their investment.
New Payment Models
Pay-for-performance is one increasingly popular strategy, Turner says. “These models typically link payments to evidence generation and clinically significant outcomes.”
A biotech company called bluebird bio, for example, offers value-based pricing for Zynteglo, a $2.8 million possible cure for the rare blood disorder known as beta thalassaemia. It generally eliminates patients’ need for blood transfusions. The company is so sure it works that it will refund 80 percent of the cost of the therapy if patients need blood transfusions related to that condition within five years of being treated with Zynteglo.
In his February 2023 State of the Union speech, President Biden proposed three pilot programs to reduce drug costs. One of them, the Cell and Gene Therapy Access Model calls on the federal Centers for Medicare & Medicaid Services to establish outcomes-based agreements with manufacturers for certain cell and gene therapies.
A mortgage-style payment system is another, albeit rare, approach. Amortized payments spread the cost of treatments over decades, and let people change employers without losing their healthcare benefits.
Only about 14 percent of all drugs that enter clinical trials are approved by the FDA. Pharma companies, therefore, have an exigent need to earn a profit.
The new payment models that are being discussed aren’t solutions to high prices, says Bill Kramer, senior advisor for health policy at Purchaser Business Group on Health (PBGH), a nonprofit that seeks to lower health care costs. He points out that innovative pricing models, although well-intended, may distract from the real problem of high prices. They are attempts to “soften the blow. The best thing would be to charge a reasonable price to begin with,” he says.
Instead, he proposes making better use of research on cost and clinical effectiveness. The Institute for Clinical and Economic Review (ICER) conducts such research in the U.S., determining whether the benefits of specific drugs justify their proposed prices. ICER is an independent non-profit research institute. Its reports typically assess the degrees of improvement new therapies offer and suggest prices that would reflect that. “Publicizing that data is very important,” Kramer says. “Their results aren’t used to the extent they could and should be.” Pharmaceutical companies tend to price their therapies higher than ICER’s recommendations.
Drug Development Costs Soar
Drug developers have long pointed to the onerous costs of drug development as a reason for high prices.
A 2020 study found the average cost to bring a drug to market exceeded $1.1 billion, while other studies have estimated overall costs as high as $2.6 billion. The development timeframe is about 10 years. That’s because modern therapeutics target precise mechanisms to create better outcomes, but also have high failure rates. Only about 14 percent of all drugs that enter clinical trials are approved by the FDA. Pharma companies, therefore, have an exigent need to earn a profit.
Skewed Incentives Increase Costs
Pricing isn’t solely at the discretion of pharma companies, though. “What patients end up paying has much more to do with their PBMs than the actual price of the drug,” Patricia Goldsmith, CEO, CancerCare, says. Transparency is vital.
PBMs control patients’ access to therapies at three levels, through price negotiations, pricing tiers and pharmacy management.
When negotiating with drug manufacturers, Goldsmith says, “PBMs exchange a preferred spot on a formulary (the insurer’s or healthcare provider’s list of acceptable drugs) for cash-base rebates.” Unfortunately, 25 percent of the time, those rebates are not passed to insurers, according to the PBGH report.
Then, PBMs use pricing tiers to steer patients and physicians to certain drugs. For example, Kramer says, “Sometimes PBMs put a high-cost brand name drug in a preferred tier and a lower-cost competitor in a less preferred, higher-cost tier.” As the PBGH report elaborates, “(PBMs) are incentivized to include the highest-priced drugs…since both manufacturing rebates, as well as the administrative fees they charge…are calculated as a percentage of the drug’s price.
Finally, by steering patients to certain pharmacies, PBMs coordinate patients’ access to treatments, control patients’ out-of-pocket costs and receive management fees from the pharmacies.
Therefore, Goldsmith says, “As long as formularies are based on profits to middlemen…Americans’ healthcare costs will continue to skyrocket.”
Transparency into drug pricing will help curb costs, as will new payment strategies. What will make the most impact, however, may well be the development of a new reimbursement system designed to handle dramatic, breakthrough drugs. As Kramer says, “We need a better system to identify drugs that offer dramatic improvements in clinical care.”
In today's podcast episode, law professor Gaia Bernstein talks about the challenges of keeping control over our thoughts and actions, even when some powerful forces are pushing in the other direction.
Each afternoon, kids walk through my neighborhood, on their way back home from school, and almost all of them are walking alone, staring down at their phones. It's a troubling site. This daily parade of the zombie children just can’t bode well for the future.
That’s one reason I felt like Gaia Bernstein’s new book was talking directly to me. A law professor at Seton Hall, Gaia makes a strong argument that people are so addicted to tech at this point, we need some big, system level changes to social media platforms and other addictive technologies, instead of just blaming the individual and expecting them to fix these issues.
Gaia’s book is called Unwired: Gaining Control Over Addictive Technologies. It’s fascinating and I had a chance to talk with her about it for today’s podcast. At its heart, our conversation is really about how and whether we can maintain control over our thoughts and actions, even when some powerful forces are pushing in the other direction.
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We discuss the idea that, in certain situations, maybe it's not reasonable to expect that we’ll be able to enjoy personal freedom and autonomy. We also talk about how to be a good parent when it sometimes seems like our kids prefer to be raised by their iPads; so-called educational video games that actually don’t have anything to do with education; the root causes of tech addictions for people of all ages; and what kinds of changes we should be supporting.
Gaia is Seton’s Hall’s Technology, Privacy and Policy Professor of Law, as well as Co-Director of the Institute for Privacy Protection, and Co-Director of the Gibbons Institute of Law Science and Technology. She’s the founding director of the Institute for Privacy Protection. She created and spearheaded the Institute’s nationally recognized Outreach Program, which educated parents and students about technology overuse and privacy.
Professor Bernstein's scholarship has been published in leading law reviews including the law reviews of Vanderbilt, Boston College, Boston University, and U.C. Davis. Her work has been selected to the Stanford-Yale Junior Faculty Forum and received extensive media coverage. Gaia joined Seton Hall's faculty in 2004. Before that, she was a fellow at the Engelberg Center of Innovation Law & Policy and at the Information Law Institute of the New York University School of Law. She holds a J.S.D. from the New York University School of Law, an LL.M. from Harvard Law School, and a J.D. from Boston University.
Gaia’s work on this topic is groundbreaking I hope you’ll listen to the conversation and then consider pre-ordering her new book. It comes out on March 28.
