Background: Erectile dysfunction (ED) is strongly influenced by persistent misconceptions that delay help-seeking and limit engagement with effective care. Patient-centered digital strategies, including generative artificial intelligence (AI) microlearning, may improve sexual-health knowledge; however, real-world evidence in urological practice remains sparse. Objective: This study aimed to evaluate whether a clinician-supervised generative AI microlearning video improves ED-related knowledge in adult men attending routine outpatient care. Methods: This single-center pre–post quasi-experimental study included 200 adult men in a university urology clinic. Participants completed an 8-item ED myth questionnaire immediately before and after watching a 3-minute educational video. The narration script was drafted using a large language model (ChatGPT) and iteratively reviewed by urologists for accuracy and cultural appropriateness. The primary outcome was the within-participant change in total correct responses (0‐8). Subgroup analyses assessed effects across age (<40 years vs ≥40 years), education level, and self-reported ED. Paired analyses and multivariable logistic regression were used (α=.05). Results: All participants completed the intervention (mean age 44.0, SD 11.6 years). Total mean correct responses increased from 3.77 to 6.56 (mean difference 2.79; <.001), indicating a large effect (Cohen =1.52). Knowledge gains were consistent across subgroups, with greater improvements among those with lower education. Self-reported ED was independently associated with lower odds of achieving ≥2-point improvement (odds ratio 0.46, 95% CI 0.26‐0.81; =.01). No adverse events or technical difficulties occurred. Conclusions: A brief clinician-supervised generative AI microlearning video was associated with substantial short-term improvements in ED myth–related knowledge in routine outpatient care. AI-assisted microlearning may represent a scalable adjunct to patient education during urological consultations. Future studies should evaluate long-term retention and behavioral outcomes.
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.
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.
“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.
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.”
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.
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.
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.
Introduction and objectivePopulations at high altitude (HA) face a higher incidence and severity of traumatic brain injury (TBI). This pilot study utilized longitudinal 1H-MRS to identify neurochemical biomarkers of HA adaptation and the subsequent metabolic response to mild TBI (mTBI).MethodsMale C57BL/6J mice were exposed to simulated HA (5,000 m) or sea level (SL) for 12 weeks. Following adaptation, a unilateral mTBI was induced via closed head injury (CHI). Mice were then monitored for an additional 2 weeks at HA (total duration of 14 weeks). In vivo1H-MRS spectra (7 T) were collected from the frontal cortex, hippocampi, and cerebellum at weeks 0, 4, 12 to assess HA adaptation. Following the CHI, subsequent measurements were collected at week 12 (post-injury) and week 14 to monitor longitudinal neurochemical responses to the mTBI.ResultsChronic HA exposure induced significant reductions in myo-inositol (Ins) and total choline (tCho) in the hippocampus, establishing a baseline of metabolic fragility that sensitized the brain to subsequent traumatic insult. Post-mTBI, the HA group exhibited a profound “metabolic crisis,” characterized by significantly lower tCho and failed recovery of total N-acetylaspartate (tNAA) compared to SL controls. Total creatine (tCr) was the most acutely affected metabolite, underscoring a depletion of the bioenergetic reserve.ConclusionChronic hypobaric hypoxia fundamentally alters baseline brain metabolism and impairs the neurochemical recovery from mTBI. These findings suggest that standard recovery protocols may be insufficient for HA-adapted populations and highlight 1H-MRS as a critical tool for detecting “invisible” metabolic vulnerability in extreme environments.
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.
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.
IntroductionIrritable bowel syndrome (IBS) is a common gut-brain axis disorder characterized by abdominal pain and altered bowel habits, and it shows high comorbidity with psychiatric disorders. However, the shared genetic mechanisms underlying these associations remain incompletely understood.MethodsWe performed a large-scale meta-analysis of IBS in individuals of European ancestry by integrating genome-wide association study (GWAS) summary statistics from the UK Biobank, Bellygenes, and the Million Veteran Program (MVP), thereby increasing statistical power to detect novel IBS loci. We further conducted global genetic correlation analyses with psychiatric traits, followed by multi-trait analysis of GWAS (MTAG) and conditional false discovery rate (condFDR) analyses to identify pleiotropic loci. Transcriptomic, methylomic, and expression quantitative trait locus (eQTL) data were integrated to explore potential regulatory mechanisms.ResultsThe meta-analysis identified up to ten previously unreported IBS loci, several of which were supported by colonic and brain eQTL effects. Global genetic correlation analyses confirmed substantial genetic overlap between IBS and psychiatric traits, particularly major depressive disorder and neuroticism. MTAG and condFDR analyses uncovered more than 100 pleiotropic loci, including signals at SORCS1, SLC35D1, COA1, and TLE1. Integrative analyses of transcriptome- and methylome-wide data highlighted regulatory mechanisms spanning colonic, immune, and neuronal tissues, supporting neuro-immune crosstalk and mitochondrial involvement.DiscussionOur findings provide a comprehensive genetic characterization of IBS, refine its heritable basis, reveal pleiotropic links with psychiatric disorders, and implicate molecular pathways across the gut-brain axis. These results advance mechanistic understanding of IBS and may inform future therapeutic development for IBS and its psychiatric comorbidities.
