Researchers Behaving Badly: Known Frauds Are "the Tip of the Iceberg"
Last week, the whistleblowers in the Paolo Macchiarini affair at Sweden's Karolinska Institutet went on the record here to detail the retaliation they suffered for trying to expose a star surgeon's appalling research misconduct.
Scientific fraud of the type committed by Macchiarini is rare, but studies suggest that it's on the rise.
The whistleblowers had discovered that in six published papers, Macchiarini falsified data, lied about the condition of patients and circumvented ethical approvals. As a result, multiple patients suffered and died. But Karolinska turned a blind eye for years.
Scientific fraud of the type committed by Macchiarini is rare, but studies suggest that it's on the rise. Just this week, for example, Retraction Watch and STAT together broke the news that a Harvard Medical School cardiologist and stem cell researcher, Piero Anversa, falsified data in a whopping 31 papers, which now have to be retracted. Anversa had claimed that he could regenerate heart muscle by injecting bone marrow cells into damaged hearts, a result that no one has been able to duplicate.
A 2009 study published in the Public Library of Science (PLOS) found that about two percent of scientists admitted to committing fabrication, falsification or plagiarism in their work. That's a small number, but up to one third of scientists admit to committing "questionable research practices" that fall into a gray area between rigorous accuracy and outright fraud.
These dubious practices may include misrepresentations, research bias, and inaccurate interpretations of data. One common questionable research practice entails formulating a hypothesis after the research is done in order to claim a successful premise. Another highly questionable practice that can shape research is ghost-authoring by representatives of the pharmaceutical industry and other for-profit fields. Still another is gifting co-authorship to unqualified but powerful individuals who can advance one's career. Such practices can unfairly bolster a scientist's reputation and increase the likelihood of getting the work published.
The above percentages represent what scientists admit to doing themselves; when they evaluate the practices of their colleagues, the numbers jump dramatically. In a 2012 study published in the Journal of Research in Medical Sciences, researchers estimated that 14 percent of other scientists commit serious misconduct, while up to 72 percent engage in questionable practices. While these are only estimates, the problem is clearly not one of just a few bad apples.
In the PLOS study, Daniele Fanelli says that increasing evidence suggests the known frauds are "just the 'tip of the iceberg,' and that many cases are never discovered" because fraud is extremely hard to detect.
Essentially everyone wants to be associated with big breakthroughs, and they may overlook scientifically shaky foundations when a major advance is claimed.
In addition, it's likely that most cases of scientific misconduct go unreported because of the high price of whistleblowing. Those in the Macchiarini case showed extraordinary persistence in their multi-year campaign to stop his deadly trachea implants, while suffering serious damage to their careers. Such heroic efforts to unmask fraud are probably rare.
To make matters worse, there are numerous players in the scientific world who may be complicit in either committing misconduct or covering it up. These include not only primary researchers but co-authors, institutional executives, journal editors, and industry leaders. Essentially everyone wants to be associated with big breakthroughs, and they may overlook scientifically shaky foundations when a major advance is claimed.
Another part of the problem is that it's rare for students in science and medicine to receive an education in ethics. And studies have shown that older, more experienced and possibly jaded researchers are more likely to fudge results than their younger, more idealistic colleagues.
So, given the steep price that individuals and institutions pay for scientific misconduct, what compels them to go down that road in the first place? According to the JRMS study, individuals face intense pressures to publish and to attract grant money in order to secure teaching positions at universities. Once they have acquired positions, the pressure is on to keep the grants and publishing credits coming in order to obtain tenure, be appointed to positions on boards, and recruit flocks of graduate students to assist in research. And not to be underestimated is the human ego.
Paolo Macchiarini is an especially vivid example of a scientist seeking not only fortune, but fame. He liberally (and falsely) claimed powerful politicians and celebrities, even the Pope, as patients or admirers. He may be an extreme example, but we live in an age of celebrity scientists who bring huge amounts of grant money and high prestige to the institutions that employ them.
The media plays a significant role in both glorifying stars and unmasking frauds. In the Macchiarini scandal, the media first lifted him up, as in NBC's laudatory documentary, "A Leap of Faith," which painted him as a kind of miracle-worker, and then brought him down, as in the January 2016 documentary, "The Experiments," which chronicled the agonizing death of one of his patients.
Institutions can also play a crucial role in scientific fraud by putting more emphasis on the number and frequency of papers published than on their quality. The whole course of a scientist's career is profoundly affected by something called the h-index. This is a number based on both the frequency of papers published and how many times the papers are cited by other researchers. Raising one's ranking on the h-index becomes an overriding goal, sometimes eclipsing the kind of patient, time-consuming research that leads to true breakthroughs based on reliable results.
Universities also create a high-pressured environment that encourages scientists to cut corners. They, too, place a heavy emphasis on attracting large monetary grants and accruing fame and prestige. This can lead them, just as it led Karolinska, to protect a star scientist's sloppy or questionable research. According to Dr. Andrew Rosenberg, who is director of the Center for Science and Democracy at the U.S.-based Union of Concerned Scientists, "Karolinska defended its investment in an individual as opposed to the long-term health of the institution. People were dying, and they should have outsourced the investigation from the very beginning."
Having institutions investigate their own practices is a conflict of interest from the get-go, says Rosenberg.
Scientists, universities, and research institutions are also not immune to fads. "Hot" subjects attract grant money and confer prestige, incentivizing scientists to shift their research priorities in a direction that garners more grants. This can mean neglecting the scientist's true area of expertise and interests in favor of a subject that's more likely to attract grant money. In Macchiarini's case, he was allegedly at the forefront of the currently sexy field of regenerative medicine -- a field in which Karolinska was making a huge investment.
The relative scarcity of resources intensifies the already significant pressure on scientists. They may want to publish results rapidly, since they face many competitors for limited grant money, academic positions, students, and influence. The scarcity means that a great many researchers will fail while only a few succeed. Once again, the temptation may be to rush research and to show it in the most positive light possible, even if it means fudging or exaggerating results.
Though the pressures facing scientists are very real, the problem of misconduct is not inevitable.
Intense competition can have a perverse effect on researchers, according to a 2007 study in the journal Science of Engineering and Ethics. Not only does it place undue pressure on scientists to succeed, it frequently leads to the withholding of information from colleagues, which undermines a system in which new discoveries build on the previous work of others. Researchers may feel compelled to withhold their results because of the pressure to be the first to publish. The study's authors propose that more investment in basic research from governments could alleviate some of these competitive pressures.
Scientific journals, although they play a part in publishing flawed science, can't be expected to investigate cases of suspected fraud, says the German science blogger Leonid Schneider. Schneider's writings helped to expose the Macchiarini affair.
"They just basically wait for someone to retract problematic papers," he says.
He also notes that, while American scientists can go to the Office of Research Integrity to report misconduct, whistleblowers in Europe have no external authority to whom they can appeal to investigate cases of fraud.
"They have to go to their employer, who has a vested interest in covering up cases of misconduct," he says.
Science is increasingly international. Major studies can include collaborators from several different countries, and he suggests there should be an international body accessible to all researchers that will investigate suspected fraud.
Ultimately, says Rosenberg, the scientific system must incorporate trust. "You trust co-authors when you write a paper, and peer reviewers at journals trust that scientists at research institutions like Karolinska are acting with integrity."
Without trust, the whole system falls apart. It's the trust of the public, an elusive asset once it has been betrayed, that science depends upon for its very existence. Scientific research is overwhelmingly financed by tax dollars, and the need for the goodwill of the public is more than an abstraction.
The Macchiarini affair raises a profound question of trust and responsibility: Should multiple co-authors be held responsible for a lead author's misconduct?
Karolinska apparently believes so. When the institution at last owned up to the scandal, it vindictively found Karl Henrik-Grinnemo, one of the whistleblowers, guilty of scientific misconduct as well. It also designated two other whistleblowers as "blameworthy" for their roles as co-authors of the papers on which Macchiarini was the lead author.
As a result, the whistleblowers' reputations and employment prospects have become collateral damage. Accusations of research misconduct can be a career killer. Research grants dry up, employment opportunities evaporate, publishing becomes next to impossible, and collaborators vanish into thin air.
Grinnemo contends that co-authors should only be responsible for their discrete contributions, not for the data supplied by others.
"Different aspects of a paper are highly specialized," he says, "and that's why you have multiple authors. You cannot go through every single bit of data because you don't understand all the parts of the article."
This is especially true in multidisciplinary, translational research, where there are sometimes 20 or more authors. "You have to trust co-authors, and if you find something wrong you have to notify all co-authors. But you couldn't go through everything or it would take years to publish an article," says Grinnemo.
Though the pressures facing scientists are very real, the problem of misconduct is not inevitable. Along with increased support from governments and industry, a change in academic culture that emphasizes quality over quantity of published studies could help encourage meritorious research.
But beyond that, trust will always play a role when numerous specialists unite to achieve a common goal: the accumulation of knowledge that will promote human health, wealth, and well-being.
[Correction: An earlier version of this story mistakenly credited The New York Times with breaking the news of the Anversa retractions, rather than Retraction Watch and STAT, which jointly published the exclusive on October 14th. The piece in the Times ran on October 15th. We regret the error.]
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."
This Special Music Helped Preemie Babies’ Brains Develop
Move over, Baby Einstein: New research from Switzerland shows that listening to soothing music in the first weeks of life helps encourage brain development in preterm babies.
For the study, the scientists recruited a harpist and a new-age musician to compose three pieces of music.
The Lowdown
Children who are born prematurely, between 24 and 32 weeks of pregnancy, are far more likely to survive today than they used to be—but because their brains are less developed at birth, they're still at high risk for learning difficulties and emotional disorders later in life.
Researchers in Geneva thought that the unfamiliar and stressful noises in neonatal intensive care units might be partially responsible. After all, a hospital ward filled with alarms, other infants crying, and adults bustling in and out is far more disruptive than the quiet in-utero environment the babies are used to. They decided to test whether listening to pleasant music could have a positive, counterbalancing effect on the babies' brain development.
Led by Dr. Petra Hüppi at the University of Geneva, the scientists recruited Swiss harpist and new-age musician Andreas Vollenweider (who has collaborated with the likes of Carly Simon, Bryan Adams, and Bobby McFerrin). Vollenweider developed three pieces of music specifically for the NICU babies, which were played for them five times per week. Each track was used for specific purposes: To help the baby wake up; to stimulate a baby who was already awake; and to help the baby fall back asleep.
When they reached an age equivalent to a full-term baby, the infants underwent an MRI. The researchers focused on connections within the salience network, which determines how relevant information is, and then processes and acts on it—crucial components of healthy social behavior and emotional regulation. The neural networks of preemies who had listened to Vollenweider's pieces were stronger than preterm babies who had not received the intervention, and were instead much more similar to full-term babies.
Next Up
The first infants in the study are now 6 years old—the age when cognitive problems usually become diagnosable. Researchers plan to follow up with more cognitive and socio-emotional assessments, to determine whether the effects of the music intervention have lasted.
The first infants in the study are now 6 years old—the age when cognitive problems usually become diagnosable.
The scientists note in their paper that, while they saw strong results in the babies' primary auditory cortex and thalamus connections—suggesting that they had developed an ability to recognize and respond to familiar music—there was less reaction in the regions responsible for socioemotional processing. They hypothesize that more time spent listening to music during a NICU stay could improve those connections as well; but another study would be needed to know for sure.
Open Questions
Because this initial study had a fairly small sample size (only 20 preterm infants underwent the musical intervention, with another 19 studied as a control group), and they all listened to the same music for the same amount of time, it's still undetermined whether variations in the type and frequency of music would make a difference. Are Vollenweider's harps, bells, and punji the runaway favorite, or would other styles of music help, too? (Would "Baby Shark" help … or hurt?) There's also a chance that other types of repetitive sounds, like parents speaking or singing to their children, might have similar effects.
But the biggest question is still the one that the scientists plan to tackle next: Whether the intervention lasts as the children grow up. If it does, that's great news for any family with a preemie — and for the baby-sized headphone industry.