Tag: News Approved

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STAT+: Access granted: CMS greenlights more than 150 participants for chronic care experiment

More than 150 companies and providers have been provisionally approved to participate in an experimental Medicare program meant to expand access to technology-supported chronic care. They include popular mental health apps, wearable device makers, a life sciences company tied to Google, and startups that help large health systems manage heart failure patients.

Announced late last year by the Center for Medicare and Medicaid Innovation, the ACCESS model will pay participants set rates to treat chronic conditions like diabetes, hypertension, high cholesterol, musculoskeletal pain, anxiety, and depression. The payments are tied to measurable health outcomes; the model is meant as an alternative to paying for individual technology services. The initial deadline to participate in the first ACCESS cohort was April 1, but CMMI Monday announced it will extend the deadline to allow more to join.

CMS officials say the large number of applications to participate in ACCESS exceeded their expectations and that the enthusiasm suggests modest payment rates and restrictions did not discourage digital health companies from applying. According to officials, most of the participants had not previously served Medicare patients. 

Continue to STAT+ to read the full story…

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STAT+: Trump goes soft on insurance, and a medical underwriting chart

This is the online version of STAT’s weekly email newsletter Health Care Inc. Sign up here.

We watched the Artemis II astronauts splash down safely last week. A reminder that legitimately amazing things can still happen. Parachute your thoughts here: bob.herman@statnews.com.

Tough talk, soft stance

A few months ago, President Trump confidently said he would be meeting with the country’s largest health insurance companies to pressure them to lower their premiums. The message was just that — a message to give the appearance that Trump officials were willing to crack down on health insurers, which have been at the center of Americans’ disdain of the health care system for decades.

Continue to STAT+ to read the full story…

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Why opinion on AI is so divided

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.

In an industry that doesn’t stand still, Stanford’s AI Index, an annual roundup of key results and trends, is a chance to take a breath. (It’s a marathon, not a sprint, after all.)

This year’s report, which dropped today, is full of striking stats. A lot of the value comes from having numbers to back up gut feelings you might already have, such as the sense that the US is gunning harder for AI than everyone else: It hosts 5,427 data centers (and counting). That’s more than 10 times as many as any other country.  

There’s also a reminder that the hardware supply chain the AI industry relies on has some major choke points. Here’s perhaps the most remarkable fact: “A single company, TSMC, fabricates almost every leading AI chip, making the global AI hardware supply chain dependent on one foundry in Taiwan.” One foundry! That’s just wild.

But the main takeaway I have from the 2026 AI Index is that the state of AI right now is shot through with inconsistencies. As my colleague Michelle Kim put it today in her piece about the report: “If you’re following AI news, you’re probably getting whiplash. AI is a gold rush. AI is a bubble. AI is taking your job. AI can’t even read a clock.” (The Stanford report notes that Google DeepMind’s top reasoning model, Gemini Deep Think, scored a gold medal in the International Math Olympiad but is unable to read analog clocks half the time.)

Michelle does a great job covering the report’s highlights. But I wanted to dwell on a question that I can’t shake. Why is it so hard to know exactly what’s going on in AI right now?  

The widest gap seems to be between experts and non-experts. “AI experts and the general public view the technology’s trajectory very differently,” the authors of the AI Index write. “Assessing AI’s impact on jobs, 73% of U.S. experts are positive, compared with only 23% of the public, a 50 percentage point gap. Similar divides emerge with respect to the economy and medical care.”

That’s a huge gap. What’s going on? What do experts know that the public doesn’t? (“Experts” here means US-based researchers who took part in AI conferences in 2023 and 2024.)

I suspect part of what’s going on is that experts and non-experts base their views on very different experiences. “The degree to which you are awed by AI is perfectly correlated with how much you use AI to code,” a software developer posted on X the other day. Maybe that’s tongue-in-cheek, but there’s definitely something to it.

The latest models from the top labs are now better than ever at producing code. Because technical tasks like coding have right or wrong results, it is easier to train models to do them, compared with tasks that are more open-ended. What’s more, models that can code are proving to be profitable, so model makers are throwing resources at improving them.

This means that people who use those tools for coding or other technical work are experiencing this technology at its best. Outside of those use cases, you get more of a mixed bag. LLMs still make dumb mistakes. This phenomenon has become known as the “jagged frontier”: Models are very good at doing some things and less good at others.

The influential AI researcher Andrej Karpathy also had some thoughts. “Judging by my [timeline] there is a growing gap in understanding of AI capability,” he wrote in reply to that X post. He noted that power users (read: people who use LLMs for coding, math, or research) not only keep up to date with the latest models but will often pay $200 a month for the best versions. “The recent improvements in these domains as of this year have been nothing short of staggering,” he continued.

Because LLMs are still improving fast, someone who pays to use Claude Code will in effect be using a different technology from someone who tried using the free version of Claude to plan a wedding six months ago. Those two groups are speaking past each other.

Where does that leave us? I think there are two realities. Yes, AI is far better than a lot of people realize. And yes, it is still pretty bad at a lot of stuff that a lot of people care about (and it may stay that way). Anyone making bets about the future on either side should bear that in mind.

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A novel behavioral paradigm using mice to study predictive postural control

Postural control circuitry performs the essential function of maintaining balance and body position in response to perturbations that are either self-generated (e.g., reaching to pick up an object) or externally delivered (e.g., being pushed by another person). Human studies have shown that anticipation of predictable postural disturbances can modulate such responses. This indicates that postural control could involve higher-level neural structures associated with predictive functions, rather than being purely reactive. However, the underlying neural circuitry remains largely unknown. To enable studies of predictive postural control circuits, we developed a novel experimental paradigm for mice. In this paradigm, modeled after studies in humans and rats, a dynamic platform generated reproducible translational perturbations. While mice stood on their hind legs atop a perch to receive water rewards, they experienced backward translations that were either unpredictable or preceded by an auditory cue. To validate the paradigm, we investigated the effect of the auditory cue on postural responses to perturbations across multiple days in three mice. These preliminary results serve to validate a new postural control experimental paradigm, opening the door to the types of neural recordings and circuit manipulations that are currently possible in mice.
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From peripheral initiation to central integration: a narrative review of the antihypertensive mechanisms of acupuncture in regulating autonomic nervous system homeostasis

Essential Hypertension (EH) is one of the most prevalent chronic cardiovascular diseases, imposing a significant burden on healthcare systems worldwide due to its high rates of disability and mortality. Long-term elevation of blood pressure leads to multi-organ damage in the heart, brain, and kidneys, resulting in severe complications such as coronary heart disease, stroke, and chronic kidney disease. Current treatment for hypertension primarily relies on pharmacological interventions. Although antihypertensive drugs have achieved notable success in controlling blood pressure, challenges remain, including poor long-term medication adherence, side effects, and inadequate blood pressure control in some patients with resistant hypertension. In parallel, acupuncture, a key modality of traditional Chinese medicine, has demonstrated unique advantages in hypertension management in recent years. Characterized by its holistic regulatory effects and minimal side effects, acupuncture is recognized by the World Health Organization as a recommended complementary and alternative therapy for hypertension, although its precise mechanisms remain incompletely understood. This review aims to summarize the “peripheral-central synergy” antihypertensive mechanism of acupuncture in regulating autonomic nervous system (ANS) homeostasis. Studies indicate that acupuncture primarily modulates autonomic homeostasis through the following pathways: (1) activating peripheral nerve fibers to convert physical stimulation into complex bioelectrical signals; (2) regulating synaptic neurotransmitter release and the expression of related membrane receptors; (3) modulating the synaptic microenvironment; (4) regulating the NTS-CVLM-RVLM neural circuit; and (5) modulating the HPA axis neuro-endocrine circuit. Through in-depth analysis, this review elucidates the multi-level and multi-dimensional impact of acupuncture therapy on primary hypertension, providing stronger evidence and a theoretical foundation for its clinical application.
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Acute aerobic exercise modulates resting-state EEG microstate dynamics in individuals with internet addiction

Internet addiction (IA) is associated with impaired cognitive control and altered large-scale brain dynamics. Electroencephalogram (EEG) microstates provide a sensitive index of rapid neural network organization; however, whether acute aerobic exercise can modulate abnormal microstate dynamics in individuals with IA remains unclear. Forty young adults [IA: n = 20; healthy controls (HC): n = 20] completed resting-state EEG recordings before and after a single 30-min bout of moderate-intensity aerobic cycling. Microstate duration, occurrence, contribution, and transition probabilities were analyzed using mixed-design repeated-measures with time (pre/post) and Microstate (A–D) as within-subject factors and group (IA/HC) as a between-subject factor. Spearman correlations examined associations between exercise-induced microstate changes and IA severity. For microstate duration, the IA group exhibited longer duration of Microstate A than HC group at baseline (t = 2.47, p = 0.018). IA group showed reduced Microstate D occurrence at baseline compared with HC (t = 4.23, p < 0.001), followed by a significant post-exercise increase (t = −4.23, p = 0.001), eliminating group differences. Microstate contribution showed a significant Time × Group interaction [F(1, 38) = 4.68, p = 0.037, η2 = 0.110], with Microstate D contribution increasing selectively in the IA group (t = −3.71, p = 0.001). Changes in Microstate D occurrence were negatively correlated with IA severity (ρ = −0.55, p = 0.012). A single session of aerobic exercise rapidly normalizes aberrant microstate dynamics in IA, particularly within Microstate D, highlighting exercise as an effective acute neuromodulatory intervention.
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In silico exploration of electric field distribution in tDCS: integrating white matter anisotropy and subject-specific structural connectivity

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique with promising application in the treatment of neurological and psychiatric disorders. However, its effectiveness is often limited by the high inter-subject variability of the induced effects, mainly attributable to individual anatomical differences, which are not considered in the design of the stimulation protocols. Among these, structural connectivity plays a crucial role but remains often overlooked in tDCS research. Objective—This study aims to evaluate how variations in structural connectivity influence the distribution of the electric field (EF) during tDCS session. In particular, we analyse how the inclusion of white matter anisotropy affects the EF distribution and spread compared to classical isotropic models, and how the strength of connection across cortical parcels affects the EF spread. Approach—The study proposes an advancement in the computational modelling of tDCS through the integration of white matter anisotropy into finite element method (FEM) simulations. By combining advanced computational approaches, we explore the relationship between EF strength and cortical connectivity. Main results—Neglecting white matter anisotropy in electromagnetic simulations lead to a relative error in EF magnitude greater than 10% and to an orientation error of the EF vector of almost 20 degrees. The DTI-informed simulations lead to a more focalized EF distribution, moreover it was found a positive and significant (p < 0.05) correlation between EF focality and the strength of connectivity between cortical areas below P2 and P1 electrodes. Significance—These findings highlight the importance of including white matter anisotropy into tDCS simulation to prevent distortions in EF distribution and suggest the need to integrate structural connectivity information into the definition of subject-specific dose in tDCS protocols.
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Causal network analysis-based assessment of gray matter alteration in post-radiotherapy nasopharyngeal carcinoma patients using 3D T1-weighted MRI

ObjectivesTo explore the temporal and causal relationships underlying brain structural changes in post-radiotherapy (RT) nasopharyngeal carcinoma (NPC) patients.MethodsA total of 38 post-radiotherapy NPC patients (33 males, 5 females; median age: 50.0 years, range: 27–63 years; median time post-RT: 17.2 months, range: 0.5–108 months) and 23 healthy controls (16 males, 7 females; median age: 37 years, range: 24–61 years) underwent T1-weighted magnetic resonance (MR) images, and their images were evaluated. The causal structural covariance network (CaSCN) analysis approach was applied to assess the causal relationships underlying radiation-induced brain structural alterations in these patients. Granger causality (GC) analysis was employed to morphometric data derived from T1-weighted MR images, which were ordered by the time elapsed post-RT.ResultsThe source-like directed associations were observed in the bilateral parahippocampal gyrus (PHG), the right gyrus rectus (REC.R), and the right caudate nucleus (CAU.R). The directed network analysis revealed that the parahippocampal gyrus (PHG), REC.R and CAU.R exhibited typical source-like characteristics, and their structural changes exerted a key regulatory effect on GM volume alterations across multiple brain regions. While the left precuneus (PCUN.L), left temporal pole: middle temporal gyrus (TPOmid.L) and the left inferior temporal gyrus (ITG.L) were typical sink-like brain region that mainly received regulatory effects from source-like brain regions, acting as major target regions of structural damage.ConclusionOver time, post-radiotherapy NPC patients exhibited progressive changes in GM volume, where the PHG.L, PHG.R, REC.R and CAU.R were core source-like brain regions. The PCUN.L, TPOmid.L, and ITG.L show distinct sink-like features, which mainly receive regulatory effects from source-like brain regions.
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ASYM: multimodal depression recognition via mamba-enhanced attentive feature fusion

IntroductionDepression is a prevalent mental disorder with a severe global impact. Traditional interview-based assessments are limited by subjectivity, lengthy procedures, and unequal access to care. Although advances in AI have facilitated multimodal models for depression detection—using audiovisual data as an accessible alternative to biosignals—current approaches remain challenged by inefficient long-term temporal modeling and superficial multimodal fusion. Moreover, biosignal-based methods are constrained by high costs and narrow applicability. These challenges underscore the urgent need for optimized multimodal solutions.MethodsThis paper proposes ASYM (Attentive Synergy Mamba), a novel multimodal architecture for depression recognition, comprising three core modules: a Cross-Modal Interactive Mamba, a Multi-Scale Gated Parallel Fusion, and a Multimodal Enhanced Mamba. First, features from each modality are interactively enhanced using convolutional neural network and Bi-Mamba blocks. Cross-modal complementary information is then extracted via a cross-attention mechanism. A dual-path fusion module subsequently augments multi-scale representations and integrates cross-modal features through dynamic weighting. Finally, the feature representations are refined by a series of Bi-Mamba blocks.ResultsEvaluations on the D-Vlog and LMVD datasets using accuracy, precision, recall, and F1-score showed that ASYM achieved an accuracy of 70.91% and an F1-score of 77.13% on D-Vlog, and 74.68% accuracy with a 74.90% F1-score on LMVD. The macro-average performance across both datasets surpassed all compared mainstream methods. Ablation studies confirmed the necessity of each component, as removing any module significantly degraded performance, underscoring the efficacy and critical contribution of the proposed architecture.DiscussionWhile multimodal depression detection has improved upon single-modality approaches, issues such as computational inefficiency in long-sequence processing and inadequate fusion strategies persist. Our model addresses these limitations through multimodal interaction and multi-scale feature fusion. Future work will focus on clinical validation across diverse populations to bridge computational psychiatry and clinical practice.
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Adverse childhood experiences and the risk of non-suicidal self-injury: a meta-analysis

BackgroundSystematically evaluate the association between Adverse Childhood Experiences (ACEs) and the risk of Non-suicidal Self-Injury (NSSI), thereby providing evidence-based guidance for relevant prevention and early intervention strategies.MethodsA systematic search was conducted across PubMed, Embase, Web of Science, and the Cochrane Library, from their inception to 30 November 2025, to identify observational studies reporting associations between ACEs and NSSI. Two researchers independently performed literature screening, data extraction, and quality assessment. Effect sizes were pooled using a random-effects model, with association strength expressed as odds ratios (OR) and their 95% confidence intervals (CI). Data analysis was conducted using Stata 15.ResultsA total of 13 articles included. The meta-analysis results suggest that physical abuse [OR = 2.38, 95% CI (1.36, 4.16), I2 = 99%], sexual abuse [OR = 1.88, 95% CI (1.24, 2.87), I2 = 94.9%], ACEs≥2 [OR = 3.23, 95% CI (2.62, 3.99), I2 = 89.9%], ACEs≥3 [OR = 6.13, 95% CI (4.07, 9.24), I2 = 96.9%], emotional abuse [OR = 1.65, 95% CI (1.18, 2.32), I2 = 97.9%] may increase the risk of NSSI.ConclusionIn summary, the findings of this meta-analysis suggest that exposure to adverse childhood experiences may be related to an increased likelihood of non-suicidal self-injury. Different forms of childhood adversity, including physical abuse, sexual abuse, and emotional abuse, as well as cumulative exposure to multiple ACEs, were associated with higher risks of NSSI.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42026128495.