Radically different
Nature Medicine, Published online: 24 April 2026; doi:10.1038/s41591-026-04301-0
A 22-year-old patient with cystic fibrosis and chronic, drug-resistant Bordetella bronchialis infection received compassionate-use phage therapy. Serum samples revealed that pre-existing antiphage immunity existed before treatment, indicating that future studies must evaluate antiphage immunity across the entire treatment regimen.
Glucagon-like peptide-1 was discovered as an insulinotropic peptide from the gut during a search for candidates for the incretin effect. It turned out to also inhibit glucagon secretion and is now considered an important regulator of glucose metabolism. In further investigations of its physiological effects, it also inhibited gastrointestinal secretion and motility and inhibited appetite and food intake. Because of these effects, it was eventually demonstrated to be able to improve glucose control and beta cell function in T2DM patients and was even associated with weight loss.
Nature Neuroscience, Published online: 24 April 2026; doi:10.1038/s41593-026-02247-7
By studying 23 neurodevelopmental disorder genes across model systems and brain cell types, the authors uncovered shared downstream effects that converge on synaptic biology, epigenetic regulation and mitochondrial function.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.
When ChatGPT was released in late 2022, it showed how easily generative AI could create human-like text. This quickly caught the eye of cybercriminals, who began using LLMs to compose malicious emails. Since then, they’ve adopted AI for everything from turbocharged phishing and hyperrealistic deepfakes to automated vulnerability scans.
Many organizations are now struggling to cope with the sheer volume of cyberattacks. AI is making them faster, cheaper, and easier to carry out, a problem set to worsen as more cybercriminals adopt these tools—and their capabilities improve. Read the full story on how AI is reshaping cybercrime.
—Rhiannon Williams
“Supercharged scams” is one of the 10 Things That Matter in AI Right Now, our essential guide to what’s really worth your attention in the field.
Subscribers can watch an exclusive roundtable unveiling the technologies and trends on the list, with analysis from MIT Technology Review’s AI reporter Grace Huckins and executive editors Amy Nordrum and Niall Firth.
Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays.
A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients? We don’t yet have a good answer—here’s why.
—Jessica Hamzelou
The story is from The Checkup, our weekly newsletter that gives you the latest from the worlds of health and biotech. Sign up to receive it in your inbox every Thursday.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 DeepSeek has unveiled its long-awaited new AI model
The Chinese company has just launched preview versions of DeepSeek-V4. (CNN)
+It says V4 is the most powerful open-source platform. (Bloomberg $)
+ And rivals top closed-source models from OpenAI and DeepMind. (SCMP)
+ The model is adapted for Huawei chip technology. (Reuters $)
2 More countries are curbing children’s social media access
Norway is set to enforce the latest ban. (Reuters $)
+ The Philippines could follow soon. (Bloomberg $)
+ Americans are pushing to get AI out of schools. (The New Yorker)
3 The US has accused China of mass AI theft as tensions rise
A White House memo claims Chinese firms are exploiting American models. (BBC)
+ Beijing calls the accusations “slander.” (Ars Technica)
4 OpenAI set itself apart from Anthropic by widely releasing its new model
It’s releasing GPT-5.5 to all ChatGPT users, despite cybersecurity concerns. (NYT $)
+ OpenAI says the new model is better at coding and more efficient. (The Verge)
5 Meta is cutting 10% of jobs to offset AI spending
Roughly 8,000 layoffs are set to be announced on May 20. (QZ)
+ Anti-AI protests are growing. (MIT Technology Review)
6 Palantir is facing a backlash from employees
Thanks to its work with ICE and the Trump administration. (Wired $)
+ Surveillance tech is reshaping the fight for privacy. (MIT Technology Review)
7 The era of free access to advanced AI is coming to an end
AI labs are under mounting pressure to start turning profits. (The Verge)
8 Elon Musk’s feud with Sam Altman is heading to court
The case has already revealed several unflattering secrets. (WP $)
9 A new movement is encouraging people to ditch their smartphones for a month
“Month Offline” is like a Dry January for smartphones. (The Atlantic)
10 Spotify has revealed its most-streamed music of the last 20 years
Featuring Taylor Swift, Bad Bunny, and The Weeknd. (Gizmodo)
Quote of the day
—Norwegian Prime Minister Jonas Gahr Store announces age restrictions for social media.
One More Thing
As astronomers have discovered more about Europa over the past few decades, Jupiter’s fourth-largest moon has excited planetary scientists interested in the geophysics of alien worlds.
All that water and energy—and hints of elements essential for building organic molecules —point to an extraordinary possibility. In the depths of its ocean, or perhaps crowded in subsurface lakes or below icy surface vents, Jupiter’s big, bright moon could host life.
To find further evidence, NASA is now searching for signs of alien existence on Europa. Read the full story on the mission.
—Stephen Ornes
We can still have nice things
A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line.)
+ Here’s a fun look at the secret collaborations of pop history.
+ Meet the mannequins showing how the “ideal” body has evolved.
+ A photographer has cataloged all 12,795 objects in her home into an archive of a life.
+ Slime molds are unexpectedly beautiful when viewed through these high-detail macro shots.
I don’t need to tell you that AI is everywhere.
Or that it is being used, increasingly, in hospitals. Doctors are using AI to help them with notetaking. AI-based tools are trawling through patient records, flagging people who may require certain support or treatments. They are also used to interpret medical exam results and X-rays.
A growing number of studies suggest that many of these tools can deliver accurate results. But there’s a bigger question here: Does using them actually translate into better health outcomes for patients?
We don’t yet have a good answer.
That’s what Jenna Wiens, a computer scientist at the University of Michigan, and Anna Goldenberg of the University of Toronto, argue in a paper published in the journal Nature Medicine this week.
Wiens tells me she has spent years investigating how AI might benefit health care. For the first decade of her career she tried to pitch the technology to clinicians. Over the last few years, she says, it’s as though “a switch flipped.” Health-care providers not only appear much more interested in the promise of these technologies, they have also begun rapidly deploying them.
The problem is that many providers aren’t rigorously assessing how well they actually work.
Take “ambient AI” tools, for example. Also known as AI scribes, they “listen” to conversations between doctors and patients, then transcribe and summarize them. Multiple tools are available, and they are already being widely adopted by health-care providers.
A few months ago, a staffer at a major New York medical center who develops AI tools for doctors told me that, anecdotally, medics are “overjoyed” by the technology—it allows them to focus all their attention on their patients during appointments, and it saves them from a lot of time-consuming paperwork. Early studies support these anecdotes and suggest that the tools can reduce clinician burnout.
That’s all well and good. But what about patient health outcomes? “[Researchers] have evaluated provider or clinician and patient satisfaction, but not really how these tools are affecting clinical decision-making,” says Wiens. “We just don’t know.”
The same holds true for other AI-based technologies used in health-care settings. Some are used to predict patients’ health trajectories, others to recommend treatments. They are designed to make health care more effective and efficient.
But even a tool that is “accurate” won’t necessarily improve health outcomes. AI might speed up the interpretation of a chest X-ray, for example. But how much will a doctor rely on its analysis? How will that tool affect the way a doctor interacts with patients or recommends treatment? And ultimately: What will this mean for those patients?
The answers to those questions might vary between hospitals or departments and could depend on clinical workflows, says Wiens. They might also differ between doctors at various stages of their careers.
Take the AI scribes, as another example. Some research on AI use in education suggests that such tools can impact the way people cognitively process information. Could they affect the way a doctor processes a patient’s information? Will the tools affect the way medical students think about patient data in a way that impacts care? These questions need to be explored, says Wiens. “We like things that save us time, but we have to think about the unintended consequences of this,” she says.
In a study published in January 2025, Paige Nong at the University of Minnesota and her colleagues found that around 65% of US hospitals used AI-assisted predictive tools. Only two-thirds of those hospitals evaluated their accuracy. Even fewer assessed them for bias.
The number of hospitals using these tools has probably increased since then, says Wiens. Those hospitals, or entities other than the companies developing the tools, need to evaluate how much they help in specific settings. There’s a possibility that they could leave patients worse off, although it’s more likely that AI tools just aren’t as beneficial as health-care providers might assume they are, says Wiens.
“I do believe in the potential of AI to really improve clinical care,” says Wiens, who stresses that she doesn’t want to stop the adoption of AI tools in health care. She just wants more information about how they are affecting people. “I have to believe that in the future it’s not all AI or no AI,” she says. “It’s somewhere in between.”
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.
Should academic journals begin to second guess guest editors?
That question gained new urgency last week when the British Medical Journal’s publishing group retracted nearly its entire guest-edited special edition of the Journal of Medical Genetics, dedicated to cancer immunotherapies. In the retraction note, the journal writes that it was, in part, because of “compromised peer review in almost all articles.” The notice garnered attention for its scope, but also because it exemplified larger concerns that research integrity advocates have with guest-edited editions, which are also called special issues in some journals.
Since I was 7, my goal has been to become a doctor. But life had other plans. I grew up in a blue-collar family in Levittown, N.Y., in the 1950s and ’60s, so it often felt like the world ended in Jersey. When I landed in Lansing, Mich., to attend Michigan State, I expected the Rocky Mountains to be visible. I ended up getting a degree in nursing, but I always had another goal: to become an M.D.
This year, at the age of nearly 73, my dream will finally come true. Soon after, I will start my residency in family medicine. My perspective on medical school and medicine is unique not only because I attended late in life, but because it came after more than 40 years as a nurse practitioner.
President Trump recently signed an executive order which aims to increase access to psychedelic drug treatments. He was joined at the signing by podcaster Joe Rogan, who said he’ ha’d messaged the president about research on the psychedelic ibogaine.
In this week’s STATus Report, host Alex Hogan chats with STAT Washington correspondent Daniel Payne about what the executive order does and doesn’t do. Hogan also looks at why ibogaine, and psychedelic drugs more broadly, are increasingly being taken seriously for stubbornly hard-to-treat conditions like addiction, depression, and PTSD.
The past four months have been a whirlwind for Pittsburgh’s journalism landscape. On Jan. 7, the Pittsburgh Post-Gazette, Western Pennsylvania’s largest news organization, announced it would cease publication on May 3 after nearly 240 years. Then, on April 14, just over two weeks before that closure date, the Baltimore-based Venetoulis Institute for Local Journalism said it would acquire the paper’s assets and continue publication.
Like many Pittsburghers, I experienced the emotional rollercoaster of anger, disappointment, hope, and relief tied to these announcements. I grew up in the Pittsburgh area, where I vividly remember running barefoot down my driveway as a child to grab the Post-Gazette. Years later, I interned there as a health and science reporter and have since contributed as a freelancer.
Psychological Review, Vol 133(3), Apr 2026, 584-618; doi:10.1037/rev0000570
A longstanding focus in the causal learning literature has been on inferring causal relations from contingencies, where these abstract away from time by collating independent instances or by aggregating over regularly demarcated trials. In contrast, individual causal learners encounter events in their daily lives that occur in a continuous temporal flow with no such demarcation. Consequently, the process of learning causal relationships in naturalistic environments is comparatively less understood. In this article, we lay out a rational framework that foregrounds the role of time in causal learning. We work within the Bayesian rational analysis tradition, starting by considering how causal relations induce dependence between events in continuous time and how this can be modeled by stochastic processes from the Poisson–Gamma distribution family. We derive the qualitative signatures of causal influence and the general computations needed to infer structure from temporal patterns. We show that this rational account can parsimoniously explain the human preference for causal models that invoke shorter, more reliable, and more predictable causal influences. Furthermore, we show this provides a unifying explanation for human judgments across a wide variety of tasks in the reanalysis of seven experimental data sets. We anticipate the framework will help researchers better understand the many manifestations of continuous-time causal learning across human cognition and the tasks that probe it, from explicit causal structure induction settings to implicit associative or reinforcement learning settings. (PsycInfo Database Record (c) 2026 APA, all rights reserved)