Science Fact vs. Science Fiction: Can You Tell the Difference?
Today's growing distrust of science is not an academic problem. It can be a matter of life and death.
Take, for example, the tragic incident in 2016 when at least 10 U.S. children died and over 400 were sickened after they tried homeopathic teething medicine laced with a poisonous herb called "deadly nightshade." Carried by CVS, Walgreens, and other major American pharmacies, the pills contained this poison based on the alternative medicine principle of homeopathy, the treatment of medical conditions by tiny doses of natural substances that produce symptoms of disease.
Such "alternative medicines" take advantage of the lack of government regulation and people's increasing hostility toward science.
These children did not have to die. Numerous research studies show that homeopathy does not work. Despite this research, homeopathy is a quickly-growing multi-billion dollar business.
Such "alternative medicines" take advantage of the lack of government regulation and people's increasing hostility toward science. Polling shows that the number of people who believe that science has "made life more difficult" increased by 50 percent from 2009 to 2015. According to a 2017 survey, only 35 percent of respondents have "a lot" of trust in scientists; the number of people who do "not at all" trust scientists increased by over 50 percent from a similar poll conducted in December 2013.
Children dying from deadly nightshade is only one consequence of this crisis of trust. For another example, consider the false claim that vaccines cause autism. This belief has spread widely across the US, and led to a host of problems. For instance, measles was practically eliminated in the US by 2000. However, in recent years outbreaks of measles have been on the rise, driven by parents failing to vaccinate their children in a number of communities.
The Internet Is for… Misinformation
The rise of the Internet, and more recently social media, is key to explaining the declining public confidence in science.
Before the Internet, the information accessible to the general public about any given topic usually came from experts. For instance, researchers on autism were invited to talk on mainstream media, they wrote encyclopedia articles, and they authored books distributed by large publishers.
The Internet has enabled anyone to be a publisher of content, connecting people around the world with any and all sources of information. On the one hand, this freedom is empowering and liberating, with Wikipedia a great example of a highly-curated and accurate source on the vast majority of subjects. On the other, anyone can publish a blog piece making false claims about links between vaccines and autism or the effectiveness of homeopathic medicine. If they are skilled at search engine optimization, or have money to invest in advertising, they can get their message spread widely. Russia has done so extensively to influence elections outside of its borders, whether in the E.U. or the U.S.
Unfortunately, research shows that people lack the skills for differentiating misinformation from true information. This lack of skills has clear real-world effects: U.S. adults believed 75 percent of fake news stories about the 2016 US Presidential election. The more often someone sees a piece of misinformation, the more likely they are to believe it.
To make matters worse, we all suffer from a series of thinking errors such as the confirmation bias, our tendency to look for and interpret information in ways that conform to our intuitions.
Blogs with falsehoods are bad enough, but the rise of social media has made the situation even worse. Most people re-share news stories without reading the actual article, judging the quality of the story by the headline and image alone. No wonder research has indicated that misinformation spreads as much as 10 times faster and further on social media than true information. After all, creators of fake news are free to devise the most appealing headline and image, while credible sources of information have to stick to factual headlines and images.
To make matters worse, we all suffer from a series of thinking errors such as the confirmation bias, our tendency to look for and interpret information in ways that conform to our intuitions and preferences, as opposed to the facts. Our inherent thinking errors combined with the Internet's turbine power has exploded the prevalence of misinformation.
So it's no wonder we see troubling gaps between what scientists and the public believe about issues like climate change, evolution, genetically modified organisms, and vaccination.
What Can We Do?
Fortunately, there are proactive steps we can take to address the crisis of trust in science and academia. The Pro-Truth Pledge, founded by a group of behavioral science experts (including myself) and concerned citizens, calls on public figures, organizations, and private citizens to commit to 12 behaviors listed on the pledge website that research in behavioral science shows correlate with truthfulness.
Signers are held accountable through a crowdsourced reporting and evaluation mechanism while getting reputational rewards because of their commitment. The scientific consensus serves as a key measure of credibility, and the pledge encourages pledge-takers to recognize the opinions of experts - especially scientists - as more likely to be true when the facts are disputed.
The pledge "really does seem to change one's habits," encouraging signers to have attitudes "of honesty and moral sincerity."
Launched in December 2016, the pledge has surprising traction. Over 6200 private citizens took the pledge. So did more than 500 politicians, including members of US state legislatures Eric Nelson (PA), James White (TX), and Ogden Driskell (WY), and national politicians such as members of U.S. Congress Beto O'Rourke (TX), Matt Cartwright (PA), and Marcia Fudge (OH). Over 700 other public figures, such as globally-known public intellectuals Peter Singer, Steven Pinker, Michael Shermer, and Jonathan Haidt, took the pledge, as well as 70 organizations such as Media Bias/Fact Check, Fugitive Watch, Earth Organization for Sustainability, and One America Movement.
The pledge is effective in changing behaviors. A candidate for Congress, Michael Smith, took the Pro-Truth Pledge. He later posted on his Facebook wall a screenshot of a tweet by Donald Trump criticizing minority and disabled children. However, after being called out that the tweet was a fake, he went and searched Trump's feed. He could not find the original tweet, and while Trump may have deleted it, the candidate edited his own Facebook post to say, "Due to a Truth Pledge I have taken, I have to say I have not been able to verify this post." He indicated that he would be more careful with future postings.
U.S. Army veteran and pledge-taker John Kirbow described how the pledge "really does seem to change one's habits," helping push him both to correct his own mistakes with an "attitude of humility and skepticism, and of honesty and moral sincerity," and also to encourage "friends and peers to do so as well."
His experience is confirmed by research on the pledge. Two research studies at Ohio State University demonstrated the effectiveness of the pledge in changing the behavior of pledge-takers to be more truthful with a strong statistical significance.
Taking the pledge yourself, and encouraging people you know and your elected representatives to do the same, is an easy and effective way to fight misinformation and to promote a culture that values the truth.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.