Tapping into the Power of the Placebo Effect
When Wayne Jonas was in medical school 40 years ago, doctors would write out a prescription for placebos, spelling it out backwards in capital letters, O-B-E-C-A-L-P. The pharmacist would fill the prescription with a sugar pill, recalls Jonas, now director of integrative health programs at the Samueli Foundation. It fulfilled the patient's desire for the doctor to do something when perhaps no drug could help, and the sugar pills did no harm.
Today, that deception is seen as unethical. But time and time again, studies have shown that placebos can have real benefits. Now, researchers are trying to untangle the mysteries of placebo effect in an effort to better treat patients.
The use of placebos took off in the post-WWII period, when randomized controlled clinical trials became the gold standard for medical research. One group in a study would be treated with a placebo, a supposedly inert pill or procedure that would not affect normal healing and recovery, while another group in the study would receive an "active" component, most commonly a pill under investigation. Presumably, the group receiving the active treatment would have a better response and the difference from the placebo group would represent the efficacy of the drug being tested. That was the basis for drug approval by the U.S. Food and Drug Administration.
"Placebo responses were marginalized," says Ted Kaptchuk, director of the Program in Placebo Studies & Therapeutic Encounters at Harvard Medical School. "Doctors were taught they have to overcome it when they were thinking about using an effective drug."
But that began to change around the turn of the 21st century. The National Institutes of Health held a series of meetings to set a research agenda and fund studies to answer some basic questions, led by Jonas who was in charge of the office of alternative medicine at the time. "People spontaneously get better all the time," says Kaptchuk. The crucial question was, is the placebo effect real? Is it more than just spontaneous healing?
Brain mechanisms
A turning point came in 2001 in a paper in Science that showed physical evidence of the placebo effect. It used positron emission tomography (PET) scans to measure release patterns of dopamine — a chemical messenger involved in how we feel pleasure — in the brains of patients with Parkinson's disease. Surprisingly, the placebo activated the same patterns that were activated by Parkinson's drugs, such as levodopa. It proved the placebo effect was real; now the search was on to better understand and control it.
A key part of the effect can be the beliefs, expectations, context, and "rituals" of the encounter between doctor and patient. Belief by the doctor and patient that the treatment would work, and the formalized practices of administering the treatment can all contribute to a positive outcome.
Conditioning can be another important component in generating a response, as Pavlov demonstrated more than a century ago in his experiments with dogs. They were trained with a bell prior to feeding such that they would begin to salivate in anticipation at the sound of a bell even with no food present.
Translating that to humans, studies with pain medications and sleeping aids showed that patients who had a positive response with a certain dose of those medications could have the same response if the doses was reduced and a dummy pill substituted, even to the point where there was no longer any active ingredient.
Researchers think placebo treatments can work particularly well in helping people deal with pain and psychological disorders.
Those types of studies troubled Kaptchuk because they often relied on deception; patients weren't told they were receiving a placebo, or at best there was a possibility that they might be randomized to receive a placebo. He believed the placebo effect could work even if patients were told upfront that they were going to receive a placebo. More than a dozen so call "open-label placebo" studies across numerous medical conditions, by Kaptchuk and others, have shown that you don't have to lie to patients for a placebo to work.
Jonas likes to tell the story of a patient who used methotrexate, a potent immunosuppressant, to control her rheumatoid arthritis. She was planning a long trip and didn't want to be bothered with the injections and monitoring required in using the drug, So she began to drink a powerful herbal extract of anise, a licorice flavor that she hated, prior to each injection. She reduced the amount of methotrexate over a period of months and finally stopped, but continued to drink the anise. That process had conditioned her body "to alter her immune function and her autoimmunity" as if she were taking the drug, much like Pavlov's dogs had been trained. She has not taken methotrexate for more than a year.
An intriguing paper published in May 2021 found that mild, non-invasive electric stimulation to the brain could not only boost the placebo effect on pain but also reduce the "nocebo" effect — when patients report a negative effect to a sham treatment. While the work is very preliminary, it may open the door to directly manipulating these responses.
Researchers think placebo treatments can work particularly well in helping people deal with pain and psychological disorders, areas where drugs often are of little help. Still, placebos aren't a cure and only a portion of patients experience a placebo effect.
Nocebo
If medicine were a soap opera, the nocebo would be the evil twin of the placebo. It's what happens when patients have adverse side effects because of the expectation that they will. It's commonly seem when patients claims to experience pain or gastric distress that can occur with a drug even when they've received a placebo. The side effects were either imagined or caused by something else.
"Up to 97% of reported pharmaceutical side effects are not caused by the drug itself but rather by nocebo effects and symptom misattribution," according to one 2019 paper.
One way to reduce a nocebo response is to simply not tell patients that specific side effects might occur. An example is a liver biopsy, in which a large-gauge needle is used to extract a tissue sample for examination. Those told ahead of time that they might experience some pain were more likely to report pain and greater pain than those who weren't offered this information.
Interestingly, a nocebo response plays out in the hippocampus, a part of the brain that is never activated in a placebo response. "I think what we are dealing with with nocebo is anxiety," says Kaptchuk, but he acknowledges that others disagree.
Distraction may be another way to minimize the nocebo effect. Pediatricians are using virtual reality (VR) to engage children and distract them during routine procedures such as blood draws and changing wound dressings, and burn patients of all ages have found relief with specially created VRs.
Treatment response
Jonas argues that what we commonly call the placebo effect is misnamed and leading us astray. "The fact is people heal and that inherent healing capacity is both powerful and influenced by mental, social, and contextual factors that are embedded in every medical encounter since the idea of treatment began," he wrote in a 2019 article in the journal Frontiers in Psychiatry. "Our understanding of healing and ability to enhance it will be accelerated if we stop using the term 'placebo response' and call it what it is—the meaning response, and its special application in medicine called the healing response."
He cites evidence that "only 15% to 20% of the healing of an individual or a population comes from health care. The rest—nearly 80%—comes from other factors rarely addressed in the health care system: behavioral and lifestyle choices that people make in their daily life."
To better align treatments and maximize their effectiveness, Jonas has created HOPE (Healing Oriented Practices & Environments) Note, "a patient-guided process designed to identify the patient's values and goals in their life and for healing." Essentially, it seeks to make clear to both doctor and patient what the patient's goals are in seeking treatment. In an extreme example of terminal cancer, some patients may choose to extend life despite the often brutal treatments, while others might prefer to optimize quality of life in the remaining time that they have.It builds on practices already taught in medical schools. Jonas believes doctors and patients can use tools like these to maximize the treatment response and achieve better outcomes.
Much of the medical profession has been resistant to these approaches. Part of that is simply tradition and limited data on their effectiveness, but another very real factor is the billing process for how they are reimbursed. Jonas says a new medical billing code added this year gives doctors another way to be compensated for the extra time and effort that a more holistic approach to medicine may initially require. Other moves away from fee-for-service payments to bundling and payment for outcomes, and the integrated care provided by the Veterans Affairs, Kaiser Permanente and other groups offer longer term hope for the future of approaches that might enhance the healing response.
This article was first published by Leaps.org on July 7, 2021.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
Generative AI has a large carbon footprint and other drawbacks. But AI can help mitigate its own harms—by plowing through mountains of data on extreme weather and human displacement.
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the bookThe Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.
Blood Test Can Detect Lymphoma Cells Before a Tumor Grows Back
David Kurtz making DNA sequencing libraries in his lab.
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.