Radically different
Category Added in a WPeMatico Campaign
After developing what he describes as the first multi-articulating, body-powered prosthetic hand, Metacarpal co-founder and CEO Fergal Mackie says he wants to solve what he sees as the biggest challenge in prosthetics. “The prosthetics industry does not have a technology problem,” he said. “It has a trust problem. Users have been let down so many…
The post Prosthetics developer Metacarpal seizes on reliability appeared first on Medical Design and Outsourcing.
Perimenopause has entered the chat. Perimenopause—and its better-known relative, menopause—used to be considered taboo. Not anymore, thanks at least in part to TV doctors and social media influencers. Perhaps it’s my age, but these days, both my algorithm and my conversations with friends increasingly swing toward perimenopause.
Menopause is defined as the life stage that occurs a year after a person has had their last period. Perimenopause is the sometimes years-long period before that point, which can also feature all the symptoms we’d typically associate with menopause.
Today, information about perimenopause is more prevalent and accessible than ever. If you’re a woman in your 40s and you’re not feeling 100%, chances are there’ll be someone online ready to tell you you’re in perimenopause. And that you might want to start spending your money on blood tests, apps, and supplements or demanding hormone replacement therapy. But as regular readers might have guessed by this point, it’s not that simple.
Perimenopause tends to start around the age of 46 or 47. It’s during this time that many women start to experience some symptoms like hot flashes, irregular or unusually heavy periods, or anxiety, for example. And it can be heavy going. “Often symptoms are at their worst in the perimenopause,” says Mary Ann Lumsden, former president of the International Menopause Society.
That’s because hormones can fluctuate wildly. Levels of estrogen, progesterone, luteinizing hormone, and follicle-stimulating hormone can roller-coaster before leveling off after menopause. And that’s why, despite what some marketers will claim, there is no test for perimenopause.
“You can’t interpret hormone [measures] because they change so much,” says Lumsden. “And that is quite normal.”
That doesn’t mean women should have to put up with symptoms. But exactly how those symptoms are treated is another topic that has been clouded by misinformation.
Last week, I told a friend about some unusually bad pelvic pain I’d experienced. Her immediate advice was to find out if I was perimenopausal and, if I was, to request hormone replacement therapy (HRT) as soon as possible. If my doctor wouldn’t prescribe it, she continued, I should simply find another doctor who would.
This line of thinking has been heavily promoted on social media platforms, says Paula Briggs, a former chair of the British Menopause Society who currently leads the menopause service at Liverpool Women’s Hospital. But it’s not helpful.
HRT is essentially designed to top up or replace hormones like estrogen and progesterone, which naturally decline around menopause. There are lots of different drugs that can be taken in lots of different ways and at various doses.
While it does come with some risks and won’t suit everyone, HRT can be immensely helpful for many menopausal women. Not only can it help with many of the common symptoms of menopause, but it can also help prevent osteoporosis and maintain muscle strength.
But these drugs were trialed in, and approved for, menopausal women, says Lumsden. They won’t have the same effects in perimenopausal women. “If you give standard HRT, it may well get swamped by [the woman’s] own hormone production,” she says.
HRT can also cause abnormal bleeding in perimenopausal women, says Briggs.
She’s concerned about the messaging on perimenopause that is being promoted on social media. Particularly worrisome, she says, is the way younger women are being encouraged to assume they are perimenopausal and seek out HRT treatment.
“It’s almost cult-like, this idea that everybody must have HRT,” she says.
And then there are the supplements. There’s been an explosion in marketing for vitamins and supplements specifically targeted to middle-aged and menopausal women. But the evidence for these, too, is either limited or nonexistent. “I can’t see a mechanism for a lot of them,” says Lumsden.
Women who take these supplements don’t always know what they’re getting. Some of Lumsden’s patients have told her they take testosterone supplements to manage their symptoms. But blood tests revealed no increase in testosterone levels. “Whatever they’re getting, it’s not testosterone,” she says.
At any rate, not all the symptoms women experience in midlife can be blamed on hormones. The lengthy lists of perimenopause symptoms shared on social media include fatigue, brain fog, aches and pains, digestive issues, and more. “These do not link closely to the obvious menstrual cycle changes and hormone changes … across menopause,” says Nanette Santoro, a professor of obstetrics and gynecology at the University of Colorado Anschutz who studies menopause.
If you’re experiencing any symptoms, it’s worth getting them checked out to make sure they’re not being caused by something else. My own pelvic pain, for example, is almost definitely the result of endometriosis—a condition that can be made worse by HRT, Lumsden tells me.
At any rate, by the time women reach their 40s, many are already juggling care for children and aging parents, often while holding down a job (and dealing with pressures from societies that don’t appear to value older women). It’s an exhausting time—and not all of that exhaustion can be blamed on hormones.
As Santoro puts it: “Attributing everything unpleasant that happens to a woman over 35 to perimenopause is not based on any scientific evidence.”
This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.
Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast.
While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use them to determine which crop variety to sow, when to fertilize, how much to invest in irrigation infrastructure, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms, as well as how to price wholesale electricity. Predictions are used to warn people about extreme weather and to trigger emergency response measures. More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.
However, the temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. These risks are relatively manageable for now, but as experts in the field, we can foresee scenarios where they snowball into far bigger, more systemic problems.
To develop weather predictions, we need accurate observations of current conditions. These are collected from several sources, including weather stations at airports, utilities, or transport services. Traditional operational systems like the Weather Research and Forecasting model or the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecasting System combine these observations with numerical approximations in order to estimate future weather patterns.
Sometimes, weather stations have issues because of, for example, instrument failures or upgrades in equipment. These can be caught either in real time (through checking and correction) or retroactively. Traditional forecasting systems also have a built-in safeguard called data assimilation: Every incoming measurement is weighed against what the physical model says should be happening and against readings from nearby stations.
Together, these mechanisms help keep weather observations reliable and predictions robust. However, new threats are putting observational accuracy at risk. Earlier this year, news outlets reported that the weather station at Paris Charles de Gaulle Airport (CDG) had been manipulated to record suspicious temperature spikes on April 6 and April 15, 2026. Authorities speculate that a hand-held hairdryer or lighter might have come into play. Either way, it led to some big payouts for online prediction-market gamblers who had bet it would hit 22 °C (71.6 °F) on days when the actual average was around 18°C (64.4°F). One individual won $20,000.
Fortunately, tampering with a single station like this can usually be caught by human monitoring or current statistical methods. In this case, members of a French climate nonprofit association noticed the anomalies by chance and raised the alarm.
But what if there are no human monitoring systems in place? And what about other types of manipulation? What if, instead of tampering with one station, someone remotely nudged the readings at many stations at once—making each change small enough to look plausible on its own? Existing quality controls struggle to catch this kind of coordinated manipulation. And time works against us; careful checks of data and metadata take hours or days, but forecasts have to go out on schedule, whatever the weather is doing.
The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; in fact, they are known as “data-driven models.” For example, researchers at ECMWF are exploring whether high-quality weather forecasts can be produced directly from raw observations, skipping the assimilation step that currently acts as a quality filter. Other researchers are going one step further; combining geospatial data (including weather station data) with large language models and agentic AI to support real-time, autonomous decision-making during extreme events such as storms.
Possible benefits are improvements in accuracy, efficiency, and speed. But removing humans from the equation introduces a vast range of new risks.
At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. One step up: A group of traders could coordinate to bias forecasts of renewable energy output, moving wholesale electricity prices and leaving whoever is on the other side of the trade holding the loss. And at the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or even keep one silent when it should sound. Step by step, the risk grows, from fraud to compromised disaster preparedness to a matter of national security.
As long as there are financial (or other) incentives to manipulate observational data, adversaries will search for new opportunities, and it is our task to stay one step ahead. Here are three ways.
1. Watch the stations. Data quality controls should include station security, anomaly detection and correction, and human oversight. Weather stations should be monitored continuously to deter tampering. Data homogenization methods that clean up weather records also need to get faster, with the goal of catching problems in real time. This will become increasingly important as agentic AI systems use these data to deliver real-time decisions. Finally, human oversight is needed to flag questionable data and model outcomes. After all, it was humans who caught the CDG Airport manipulation.
2. Protect the data to safeguard the AI. Data defense mechanisms must be positioned throughout the AI pipeline. AI explainability and adversarial robustness tools can help us understand the underlying data and the AI model outputs, help us identify data- or model-related issues, and potentially make us more resilient to adversarial attacks.
3. Ensure continuous accountability along the chain. Observational data passes through many hands: the operators who run the stations, the national weather services that steward the records, and the forecasting centers that turn them into predictions. No single one of them can protect data integrity alone—each guards its own link, and any anomaly needs to be communicated along the whole chain, from station operators to the people acting on the forecast.
It is fortunate that the situation at CDG Airport was caught, but it should serve as a wake-up call. As the role of observational data grows in weather forecasting, we need to adapt to evolving threats. This means protecting our data and models by strengthening existing oversight and accountability structures, and improving coordination among key partners.
This op-ed was written by:
For the last year, a small California startup has been making extraordinary claims about the ability of its technology to potentially treat Duchenne muscular dystrophy — and maybe a slew of other genetic diseases, too.
The company, Sonothera, does not yet have clinical data. But it’s presented data in animals so stunning that other experts can’t quite wrap their minds around it.
“I find it hard to believe,” said Eric Olson, molecular biology chair at UT-Southwestern Medical Center. “It seems a bit too good to be true,” said Jeffrey Chamberlain, a longtime Duchenne gene therapy expert at the University of Washington.
At a May MAHA Institute summit organized around the theme of “overmedicalization,” the health secretary announced an action plan to promote psychiatric deprescribing. At first look, it seemed innocuous. The Substance Abuse and Mental Health Services Administration (SAMHSA) would study prescribing trends and publish fact sheets. Medicare would clarify how clinicians can be paid for the attentive work of tapering a patient off of a medication (which is already a part of routine clinical care). Webinars would teach prevention and “holistic” care. A technical expert panel would convene over the summer to make further recommendations.
In reality, this announcement, and the steady stream of actions over the past 18 months, mark a quiet rewriting of the vocabulary of American mental health care — a massive rhetorical shift enacted while programs and protections that would actually solve the problem are dismantled.
Every day, doctors turn to specialized calculators to make decisions about their patients’ care. Kidney performance? There’s a calculator for that. Chance of a successful vaginal birth after a previous C-section? There’s a calculator for that.
Medicine has accumulated hundreds of these clinical scores and decision-making tools, and their numbers continue to grow along with the scale of clinical data. But just because a calculator exists doesn’t mean doctors should always trust its output.
“In clinical practice, a lot of the tools that we use, we genuinely have no idea how limited it is in its validation,” said health systems researcher and gastroenterologist Shazia Siddique. That is why MDCalc, the company Siddique joined last year, is launching a quality-rating system to apply to the more than 800 clinical tools and calculators that doctors use through its site.
Specialized counseling services for LGBTQ+ youth will return to the 988 Suicide & Crisis Lifeline by the end of the year, the Trump administration confirmed last month. But young people looking to “press 3” for that support may encounter an altered experience, as federal health officials want to ensure the services comply with President Trump’s executive order last year that essentially denies the existence of transgender and nonbinary identities.
The Trump administration shuttered the LGBTQ+ youth specialty services last July, but soon after, a congressional appropriations bill directed $33.1 million toward reinstating the line. The law indicates that services should support all LGBTQ+ youth.
It’s easy to understand how power tools make joint replacements easier for orthopedic surgeons, but how do you quantify the improvement of such an innovation with hard evidence? When Johnson & Johnson MedTech’s DePuy Synthes launched its Kincise Surgical Automated System for total hip arthroplasty (THA) in 2020, it used surface electromyography (sEMG) to measure…
The post Sensors on surgeons helped J&J’s DePuy Synthes sell a new tool appeared first on Medical Design and Outsourcing.