Dear Readers,
I want to bring your attention to “The Deadliest Drug,” a new STAT investigative series about our nation’s failure to address excessive alcohol use, and how it goes unimpeded as a top cause of disease, injury, and death.
Wireless Stress Detector Offers Multiple Medical Uses
A next-generation device that detects signs of stress could have wide-ranging applications, from investigating sleep disorders to detecting signs of sepsis.
The polygraph detector, described in Science Advances, is worn on the chest and can even sense when a person is lying.
It allows psychophysiological states to be continuously monitored through a combination of multimodal sensing and wireless data transmission.
The gadget offers an alternative to current approaches such as such as polygraphy and polysomnography (PSG), which involve cumbersome wired sensors that limit their practicality.
“By uncovering mechanistic links between autonomic imbalance, stress reactivity, and health outcomes, these devices have the potential to transform diagnostic workflows, optimize educational programs, and enable personalized therapeutic monitoring across stress medicine, pediatrics, and behavioral health,” reported Sun Hong Kim, PhD, from the University of Seoul in South Korea, and co-workers.
Subtle physiological variations in cardiac, respiratory, electrodermal, and thermal activity often serve as indicators of compromised health or heightened stress responses.
These can be reflected in many scenarios, from pediatric sleep disorders that disrupt neurodevelopment to the psychological strain experienced in high-stakes clinical settings or during polygraph examinations.
Accurate monitoring of psychophysiological states is therefore essential for understanding how stress and autonomic dysfunction manifest across a wide spectrum of medical conditions.
However, most existing devices monitor only one or two parameters or rely on electrochemical sensors that detect sweat biomarkers, thereby failing to reflect the complex and dynamic interplay between multiple physiological systems.
Kim and co-workers therefore designed a single platform to enable comprehensive assessment of autonomic and stress-related physiology in real time.
The device continuously measures changes in heartbeat, skin temperature, and breathing, which are then converted using machine learning into measures of psychological strain.
The device had high fidelity with gold standard systems in quantifying the complex psychological stress induced by polygraph interviews and complex cognitive load tasks as well as the physical stress caused by repeatedly putting a hand in an iced water.
During overnight monitoring of children, it reliably identified arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures among infants with Down syndrome.
Real-world deployment during emergency simulation training showed that multimodal stress signatures correlate inversely with performance, reflecting its value for medical education.
Machine learning analyses across all studies confirmed that multimodal features outperformed single-signal approaches in detecting stress and clinical events with high sensitivity and specificity.
“A particularly notable contribution lies in pediatric sleep medicine,” the authors noted.
“Simultaneous comparison with PSG confirms the ability to detect arousals, hypopnea, and apnea while also providing mechanistic insights into autonomic regulation.
“In infants with Down syndrome, multimodal analysis reveals attenuated sympathetic responsiveness and parasympathetic dominance, consistent with known vulnerabilities in airway patency and autonomic control.
“Such disease-specific autonomic signatures may serve as valuable biomarkers for risk stratification, early diagnosis, and targeted intervention in neurodevelopmental disorders.”
The post Wireless Stress Detector Offers Multiple Medical Uses appeared first on Inside Precision Medicine.
STAT+: CDC plans to transfer monkeys to nonprofit’s sanctuary as it seeks to reduce animal testing
As part of efforts to phase out the use of monkeys in research, the Centers for Disease Control and Prevention intends to transfer more than 160 macaques to Born Free USA, a nonprofit that runs a large primate sanctuary in Texas.
The agency is trying to move quickly due to the “unusual and compelling urgency” of finding housing for the monkeys, according to a notice about the proposed contract that was posted on a procurement website run by the General Services Administration.
A timeline for the transfer was not specified, but the agency is accepting responses until May 28.
Contact Lenses Show Promise for Depression
Using specialized contact lenses to stimulate the brain could offer a novel route to treating depression, preclinical research suggests.
The research, in mice, demonstrates how wearable neuromodulation devices can provide a versatile platform for mood and other brain disorders.
It brings eye-based neurotherapies a step closer towards clinical reality and reveals the feasibility of using contact lenses as a bioelectronic strategy for the treatment of depression.
The findings appear in the latest issue of Cell Reports Physical Science.
“Our work opens up an entirely new frontier of treating brain disorders through the eye,” said lead author Jang-Ung Park, PhD, from Yonsei University.
“We believe this wearable, drug-free approach holds tremendous promise for transforming how depression and other brain conditions are treated, including anxiety, drug addiction, and cognitive decline.”
Depression is increasingly recognized as a disorder involving structural and functional abnormalities in brain networks.
Conventional treatments—such as pharmacological therapy, electroconvulsive therapy, and deep brain stimulation—target these abnormalities but can be invasive and are often limited in their efficacy or tolerability.
Park and team note that the eye provides a compelling gateway for indirect brain modulation due to its embryological derivation from the brain and extensive connectivity.
Studies also suggest that visual impairment with higher prevalence of depression, further recognizing the importance of the eye-brain axis.
To investigate this avenue further, the researchers developed a contact lens that uses transcorneal electrical stimulation (TES) based on temporal interference (TI) to stimulate the brain. This delivers two electrical signals to the retina, which only become active where they intersect, allowing specific areas of the brain to be targeted.
The platform circumvents the invasiveness and limited tolerability of conventional brain stimulation therapies by using the retina as a precise interface for the eye-brain axis.
Electrodes made from ultrathin layers of gallium oxide and platinum allow the lens to be flexible and transparent, conforming to the cornea and preserving natural vision.
The researchers examined the efficacy of the lenses in a stress-induced mouse model that recapitulated key behavioral and biological features associated with depression.
Depressed mice received either no intervention, temporal interference, or the SSRI fluoxetine and were compared with control mice that were not depressed before and after treatment. Machine learning was applied for comprehensive efficacy evaluation.
The team reported that the lenses restored behavioral, neural, and biological deficits in depression.
TI-TES enhanced behavioral resilience, restored prefrontal-hippocampal oscillatory synchrony, and normalized depression-related biomarkers.
When machine-learning integration was used to integrate behavior, brain activity, and biomarkers, it consistently grouped the mice with lenses with the non-depressed control mice rather than the untreated depressed mice.
The researchers acknowledge their research is in its early stages, and that the current study employed a wired configuration to ensure precise waveform control and stimulation stability during proof-of-concept validation.
“Like any new medical technology, our contact lenses will need to go through rigorous clinical evaluation in patients before reaching the market,” said Park.
“Next, we plan to make the lens fully wireless, test it for long-term safety in larger animals, and personalize the stimulation for each user before advancing into clinical trials in patients.”
The post Contact Lenses Show Promise for Depression appeared first on Inside Precision Medicine.
<![CDATA[Key schizophrenia facts: early warning signs, brain changes, treatment limits—and how AI could reveal biomarkers for more personalized care.]]>
Establishing AI and data sovereignty in the age of autonomous systems
When generative AI first moved from research labs into real-world business applications, enterprises made a tacit bargain: “Capability now, control later.” Feed your proprietary data into third-party AI models, and you will get powerful results. But your data passes through systems you do not own, under governance you do not set. The protections you rely on are only as durable as the provider’s next policy update.
Now, with generative AI established in everyday business operations and sophisticated new agentic AI systems advancing every day, companies are reevaluating the terms of that deal.
“Data is really a new currency; it’s the IP for many companies,” says Kevin Dallas, CEO of EDB, echoing a recurrent anxiety from customers. “The big concern is, if you’re deploying an AI-infused application with a cloud-based large language model, are you losing your IP? Are you losing your competitive position?”
That question is now fueling a movement toward reclaiming both the data and AI systems that have rapidly become part of core business infrastructure. AI and data sovereignty, which refers to breaking dependence on centralized providers and establishing genuine control over models and data estates, it is an urgent priority for many companies, says Dallas, citing internal EDB data: “70% of global executives believe they need a sovereign data and AI platform to be successful.”
The idea of AI sovereignty is becoming a global policy conversation. NVIDIA CEO Jensen Huang recently spoke about the need for such a shift at the World Economic Forum’s annual meeting at Davos in January 2026: “I really believe that every country should get involved to build AI infrastructure, build your own AI, take advantage of your fundamental natural resource—which is your language and culture—develop your AI, continue to refine it, and have your national intelligence be part of your ecosystem.”
This report explores how enterprises are pursuing sovereignty over their models and data estates in an era of rapid AI adoption. Drawing on a survey conducted by EDB of more than 2,050 senior executives and a series of interviews with industry experts, the research confirms that the sovereignty movement on the enterprise level is already well underway.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Data readiness for agentic AI in financial services
Financial services companies have unique needs when it comes to business AI. They operate in one of the most highly regulated sectors while responding to external events that are updated by the second. As a result, the success of agentic AI in financial services depends less on the sophistication of the system and more on the quality, security, and accessibility of the data it relies on.
“It all starts with the data,” says Steve Mayzak, global managing director of Search AI at Elastic.
Agentic AI—systems that can independently plan and take actions to complete tasks, rather than simply generate responses—holds enormous potential for financial services due to its ability to incorporate real-time data and optimize complex workflows. Gartner has found that more than half of financial services teams have already implemented or plan to implement agentic AI.
However, introducing autonomous AI into any organization magnifies both the strengths and weaknesses of the underlying data it uses. To deploy agentic AI with speed, confidence, and control, financial services companies must first be able to search, secure, and contextualize their data at scale. “Agentic AI amplifies the weakest link in the chain: data availability and quality,” says Mayzak. “And your systems are only as good as their weakest link.”
Financial services companies, therefore, require a trusted and centralized data store that is easy to access, dependable, and can be managed at scale.
The high stakes of quality information
Regulation in the financial services sector requires a high degree of accountability for all data tools. As Mayzak says, “You can’t just stop at explaining where the data came from and what it was transformed into: ‘Here’s the data that went in, and this is what came out.’ You need an auditable and governable way to explain what information the model found and the logic of why that data was right for the next step.” That is, you need to be able to see, understand, and describe the underlying processes.
At the same time, financial services companies require speed and accuracy in order to meet customer expectations and stay ahead of competition. Markets are continually shifting, and risks and opportunities move along with them. If an AI model can parse natural language (unstructured data) from complex sources—in addition to structured data in spreadsheets that are easier to analyze—this gives users more relevant information.
In this environment, there is no tolerance for error, including the hallucinations that plagued early AI efforts. Agentic AI systems depend on rapid access to high-quality, well-governed data that is secure and accessible. In financial services, that data spans transactions, customer interactions, risk signals, policies, and historical context. The task of preparing that data for AI should not be underestimated. “Natural language is way more messy than structured data, and that makes the process of organizing and cleaning it up that much more important and also that much harder,” says Mayzak.
The data must be well indexed and consolidated across different locations, not locked in the silos of separate systems across the organization. Otherwise, AI agents lag, provide inconsistent answers, and produce decisions that are harder to trace and explain, undermining confidence among regulators, customers, and internal stakeholders.
As Mayzak says, “There are many different ways to describe how to execute a trade at a bank. In an agent-powered world, we need those descriptions to be deterministic—to give the same results every time. Yet we’re building on powerful but non-deterministic models. That’s incredibly tricky, but not impossible.”
For a financial services firm, managing this can be very challenging. A Forrester study found that 57% of financial organizations are still developing the necessary internal capabilities to fully leverage agentic AI. “The data exists in many different formats, created over the course of a bank’s history,” says Mayzak. “Take any bank that’s been around for 50 years: They might have 60 different types of PDFs for the exact same thing. And at the same time, we want the output of these systems to be 100% accurate. In many cases, there is no ‘good enough’.” That is, companies need to do it right, and the first time.
Searching and securing results
An effective search platform is key to solving the problem of fragmented, poorly indexed, inaccessible data. Financial services companies that can readily sift through both their structured and unstructured data, keep it secure, and apply it in the right context will get the most value from agentic AI. This often requires designing AI systems with data access and utility in mind so they can work faster and yield more accurate results, as well as reduce risk. “Search is the foundational technology that makes AI accurate and grounded in real data,” Mayzak says. “Search platforms have become the authoritative context and memory stores that will power this AI revolution.”
Once in place, these AI-enhanced searches and autonomous systems can serve financial services companies for a range of purposes. When monitoring client exposure, agentic AI can continuously scan transactions, market signals, and external data to detect emerging risks; platforms can then automatically flag or escalate issues in real time. In trade monitoring, AI agents can review trade workflows, identify discrepancies across different formats, and resolve exceptions step by step with minimal human intervention. In regulatory reporting, AI can gather data from across systems, generate required reports, and track how each output was produced. These applications of AI save time while supporting audit and compliance needs by being traceable and explainable.
Although such capabilities already exist, they are often manual, fragmented, and difficult to scale. Agentic AI allows financial organizations to move toward more automated, efficient, and scalable processes while maintaining the accuracy and transparency required in their highly regulated environment. As Mayzak says, “It’s not that different from how humans operate today, just done at a much faster pace and at scale.”
Building an agentic AI ecosystem
Launching agentic AI can be daunting, especially if other AI ventures have stalled internally. Mayzak’s recommendation is to choose a manageable use case and allow it to grow over time. “Success can build on success,” he says. “While companies may aim to automate a 70-step business process, they are discovering that you have to start somewhere. What is working in the market is tackling the problem one step at a time. Once you get the first step working, then you can take the next step, and the next.”
The financial services organizations that lead among their peers will be those that integrate agentic AI into a broader ecosystem that includes strong security controls, good data governance, and effective management of system performance. As Mayzak says, “Doing this well will create an AI feedback loop, where executives gain new signals from these systems to assess the effectiveness of their investments and generate reliable, actionable insights.” By iterating on pilots and continuously improving, companies will build agentic systems that can be measured, managed, and scaled. This will transform agentic AI into lasting competitive advantage.
Learn more about how Elastic supports financial services.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
The Download: deepfake porn’s stolen bodies and AI sharing private numbers
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.
The shock of seeing your body used in deepfake porn
When Jennifer got a research job in 2023, she ran her new professional headshot through a facial recognition program. She wanted to see whether it would pull up the porn videos she’d made more than a decade earlier. It did, but it also surfaced something she’d never seen before: one of her old videos, now featuring someone else’s face on her body.
Conversations about sexualized deepfakes usually focus on the people whose faces are inserted into explicit content without consent. But another group often gets ignored: the people whose bodies those faces are attached to.
Adult content creators say AI systems are training on their work, cloning their likenesses, and generating explicit content they never agreed to make, all with little legal protection or control. Read the full story on the threat to their rights, livelihoods, and ownership of their own bodies.
—Jessica Klein
This story is part of our The Big Story series, the home for MIT Technology Review’s most important, ambitious reporting. You can read the rest here.
AI chatbots are giving out people’s real phone numbers
Generative AI is exposing people’s personal contact information—and there’s no easy way to stop it.
A software developer started receiving WhatsApp messages asking for help after Gemini surfaced his number. A university researcher got the chatbot to reveal a colleague’s private cell number. A Reddit user says Gemini sent a stream of callers looking for lawyers to his phone.
Experts believe these privacy lapses stem from personally identifiable information in AI training data. Chatbots may now be making that information dramatically easier to find.
Find out why these breaches are growing—and why there’s little that victims can do to stop them.
—Eileen Guo
The Tesla Semi could be a big deal for electric trucking
Nearly a decade after Elon Musk first unveiled the Tesla Semi, the electric truck is finally rolling off the production line. It could be a breakout moment for battery-powered freight.
Semitrucks produce an outsized share of road transport pollution, while electric alternatives have struggled with high prices, limited range, and charging challenges. Tesla is betting the Semi can overcome those problems. The truck reportedly travels up to 480 miles on a single charge and costs far less than many competing electric models.
Here’s how the Tesla Semi could give electric trucking a vital boost.
—Casey Crownhart
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 The US has approved Nvidia chip sales to 10 Chinese firms
Alibaba, Tencent, and ByteDance are among those cleared to buy H200 chips. (Reuters $)
+ The US will receive 25% of the revenue from the sales. (Engadget)
+ But Beijing wants domestic firms to prioritize homegrown chips. (Nikkei Asia)
+ Nvidia CEO Jensen Huang is in China with a White House delegation. (CNBC)
2 Beijing’s push for AI independence is weakening US leverage
It’s allowing China to resist pressure during the Beijing talks. (NYT $)
+ The country has made a big bet on open-source. (MIT Technology Review)
+ Here’s what’s at stake for tech at the Trump-Xi meeting. (Rest of World)
3 AI is “rotting the brains” of developers
They’re losing their previous abilities to do their jobs. (404 Media)
+ A populist backlash is building against AI. (MIT Technology Review)
+ It’s time to reset our expectations about AI. (MIT Technology Review)
4 Sam Altman has over $2 billion in companies that have dealt with OpenAI
The ties have triggered accusations of conflicts of interest. (The Times $)
+ The GOP is scrutinizing Altman’s business dealings. (WSJ $)
5 Andreessen Horowitz has become the top political donor in the US
A16z contributed $115.5 million to the midterm elections. (NYT)
+ AI lobbying has reached a fever pitch. (NYT $)
6 Microsoft feared being too dependent on OpenAI
CEO Satya Nadella was worried about OpenAI supplanting his company. (CNBC)
+ Microsoft is eyeing startup deals for life after OpenAI. (Reuters $)
7 AI systems are forecasting wars and regime collapse
One estimates a 20% chance of regime change in Iran by 2026. (Economist $)
+ AI has turned the Iran conflict into theater. (MIT Technology Review)
8 Anthropic says a model behaved badly due to training on dystopian sci-fi
Training on more positive stories could help. (Ars Technica)
9 Data centers now consume 6% of the electricity in the US and UK
AI’s global energy consumption is up 15% globally in two years. (Guardian)
10 NASA has rescued Curiosity after its drill got stuck on Mars
The agency has just revealed how it freed the rover. (Wired $)
Quote of the day
“Musk loves to be glazed, and this person is the doughnut factory.”
—Joan Donovan, assistant professor of journalism and emerging media studies at Boston University, tells the Washington Post how Elon Musk has consistently amplified one anonymous X account.
One More Thing
YOSHI SODEOKA
Inside the messy ethics of making war with machines
In a near-future war—one that might begin tomorrow—a sniper’s computer vision system flags a potential target. Just over the horizon, a chatbot advises a commander to order an artillery strike.
In both cases, an AI system recommends pulling the trigger while a human still has the final say. But how much of the decision is really theirs? When, if ever, is it ethical for that decision to kill? And who’s to blame when something goes wrong?
This is how AI is reshaping decision-making on the battlefield.
—Arthur Holland Michel
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.)
+ The secrets behind how Shazam works have been revealed.
+ For the first time in a decade, a rare “Cloud Jaguar” was caught on camera.
+ Explore our galaxy from your screen at this year’s Milky Way Photographer of the Year collection.
+ If you want a game over with style, a funeral company is offering Mario, Luigi, Peach, and even Yoshi-branded coffins.
Overcoming the blood–brain barrier in Alzheimer’s disease: translational perspectives on advanced drug delivery platforms
Alzheimer’s disease (AD) is the leading cause of dementia worldwide and represents a growing public health challenge in aging societies. Despite extensive research efforts, currently approved therapies provide only limited symptomatic benefit and do not halt disease progression. A major obstacle to effective treatment is the blood–brain barrier (BBB), which severely restricts the brain delivery of most therapeutic agents. Nanoparticle-based drug delivery systems have emerged as a promising strategy to overcome BBB-related limitations by enabling precise control over physicochemical properties such as size, surface characteristics, and material composition. These properties can improve drug solubility, stability, pharmacokinetics, and targeted brain accumulation while reducing systemic toxicity. However, efficient BBB penetration and clinically feasible translation remain major challenges. This review summarizes key design principles for nanoparticles intended for AD therapy and highlights representative platforms with translational considerations, particularly lipid-based and polymer-based nanoparticles. In addition, alternative delivery strategies—including nose-to-brain nanoparticle systems and nanoparticles exploiting receptor-mediated and adsorptive-mediated transcytosis, as well as synaptic dysfunction targeting—are discussed. Collectively, this review outlines current advances and future directions for nanoparticle-mediated therapeutic delivery in AD.
Natural head orientation and spatial hearing with symmetric frontal maskers
The purpose of this study was to evaluate the effect of natural, undirected head orientation on speech perception in the presence of interfering speech maskers that were symmetrically arranged to minimize the better-ear advantage. We also examined the characteristics of natural head motion under these conditions. Fourteen normal-hearing adults participated in continuous number categorization tasks under both head-fixed and head-free conditions. Three parameters were measured across different spatial listening configurations: (1) speech reception threshold (SRT) in both co-located and spatially separated masker conditions (±30° azimuth), (2) accuracy with spatially separated maskers at a fixed target-to-masker ratio (TMR), and (3) functional spatial boundary (FSB) in an adaptive masker-location condition. No significant differences in speech perception performance were observed between head-fixed and head-free conditions across all listening configurations. However, performance changes relative to the head-fixed condition were significantly correlated with head-orientation magnitude in the co-located SRT and FSB conditions. Exploratory analyses further indicated that larger head rotations were sometimes associated with performance improvements, whereas smaller rotations occasionally accompanied performance decrements. These observations may reflect complex interactions between dynamic spatial cues produced by head motion and moment-to-moment variations in task engagement. However, these observations warrant cautious interpretation and may provide a basis for future investigations into the role of natural head movement in spatial listening.