Radically different
Background: Mobile mental health apps may provide an accessible, scalable, and private avenue for older veterans who may not otherwise seek or receive care to address their mental health concerns. However, older veterans may experience barriers to using these apps that need to be addressed to facilitate effective use. Such support could be effectively implemented within the US Veterans Health Administration to facilitate the use of the United States Department of Veterans Affairs’ established mental health apps and to benefit older veterans with mental health concerns. Objective: This study aimed to (1) assess older veterans’ interest in and barriers to using mental health apps to address problems such as difficulties with social connection, and (2) develop and pilot a coaching program to address barriers that older veterans experience in using mobile devices and apps. Methods: Rapid qualitative analysis of semistructured qualitative interviews with 12 older veterans identified themes regarding interest, barriers, and preferences for support for using mobile apps. These themes informed the development of a coaching program, which was piloted with 13 older veterans to assess acceptability, feasibility, and resultant signals of changes in mobile device proficiency. Results: Most veterans expressed interest in using mental health apps. One of the most common barriers was familiarity and proficiency with mobile devices and app technology. Other common barriers included usability or accessibility of the technology or app, motivation, and memory. Veterans reported interest in receiving coaching support. Though the majority of veterans expressed some preference for more individualized and in-person support, they identified both benefits and drawbacks to all potential coaching modalities (group vs one-on-one, in-person vs remote)—including issues of individualizable and guided assistance, feasibility and accessibility of the support, and group settings as potential avenues for social connection as well as potential susceptibility to challenging social dynamics and interactions. Mobile Device and App Learning (MoDAL)—a 2-session, interactive, remote educational group—was developed and piloted. Most veterans who participated found MoDAL helpful. Participants’ mobile device proficiency showed a statistically significant improvement on average pre- to post-MoDAL, although this effect was small, and the small sample size limits the strength of the conclusions. Conclusions: Older veterans do have some interest in using mobile mental health apps to address mental health–related issues. However, they experience critical barriers, including a lack of familiarity and proficiency with the technology. MoDAL may improve older veterans’ comfort and proficiency with mobile devices and apps, which could address one of the barriers that impacts downstream engagement in mental health apps and other virtual care modalities.
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Research findings help explain why symptoms present so differently from one child to the next, and why individualized supports and interventions are essential.
Autism can look very different from person to person. One child might differ from another in how they learn, process sensory information, and experience social and communication challenges. Scientists have long suspected these differences stem from distinct biology, but proving it has been challenging — until now.
A recent study published in Nature Neuroscience has identified two biological subtypes of autism linked to different pathways in the brain.
Researchers from the Child Mind Institute, the Istituto Italiano di Tecnologia, and other international partners analyzed brain connection patterns in nearly 2,000 individuals, including 940 autistic people from the Autism Brain Imaging Data Exchange (ABIDE). By combining human brain-imaging datasets with complementary biological data, they identified two consistent patterns in how different brain regions communicate.
One subtype showed reduced communication, or hypoconnectivity, among brain regions linked to pathways that help brain cells send signals to one another. The other showed increased communication, or hyperconnectivity, among brain regions linked to pathways associated with the immune system. The two subtypes exhibited differences in functional brain structure and modest differences on standardized autism assessments, with the hyperconnectivity subtype scoring moderately higher on autism severity measures.
These findings give scientists the first empirically biology-based framework for understanding autism’s complexities over time. This type of work could move the field closer to more precise, personalized approaches to medicine and care. However, this does not mean autism can now be divided into just two categories, nor does it create a new diagnostic framework. Autism is complex, and these two subtypes are likely part of a much larger picture.
The study also highlights the importance of open science. Through shared datasets like ABIDE, researchers can tackle questions too large for a single lab to answer alone.
The post New Study Identifies Different Biological Subtypes of Autism appeared first on Child Mind Institute.
RFK Jr. adviser Calley Means and Kennedy’s son Finn attended the Enhanced Games, the pro-doping athletic competition and biohacking extravaganza that took place over the weekend in Las Vegas, according to The Washington Post. Send news tips and personal bests to John.Wilkerson@statnews.com or John_Wilkerson.07 on Signal.
For weeks, Republicans have been preoccupied with an immigration funding bill that they’re pushing through Congress, without support from Democrats. I’ve not been writing about that bill because it doesn’t include health care policies. But it’s now becoming relevant to health care, albeit indirectly.
Early last week, Republicans were expected to pass that budget reconciliation bill without much friction. By the end of the week, Senate Republicans adjourned for a week-long recess without voting on it due to an impasse over a new $1.8 billion settlement fund for Trump’s allies. They’d also butted heads with the president over his demands for $1 billion for a White House complex and ballroom.
Background: Returning migrants face a variety of challenges that limit their ability to integrate and adapt to Mexico. This represents a break in their life trajectory, with effects on family dynamics, mid- and long-term projects, and uncertainty about short-term plans. Objective: This study describes the coproduction approach used to design and develop a WhatsApp-based psychoeducational program entitled “Here Again: Coping With Return,” which aims to promote the adoption of self-care behaviors to reduce the risk of mental health and substance use problems among returning migrants. Methods: The process included four phases: (1) a situational diagnosis of the needs of migrants in preventing mental health problems and reducing the risks associated with alcohol use, (2) the design and development of content, (3) evaluation by a group of experts in mental health and substance use, and (4) pilot testing. Results: The study identified 4 intervention pillars: emotional risk factors, coping strategies, barriers to care, and technological feasibility. Eighty WhatsApp messages were developed, focusing on mental health (n=52, 65%) and alcohol use (n=20, 25%) through a sequence of motivation, instruction, and reinforcement. Following an expert evaluation that simplified technical language, a pilot study with 14 migrants showed a 78.6% completion rate. Participants reported the successful application of emotional management tools and a preference for text-based messages over audiovisual content to conserve mobile data. Conclusions: This study describes the development of a psychoeducational program for returning migrants based on coproduction, integrating user needs and expert experience. The intervention addresses emotional management, self-care, and substance use prevention, using WhatsApp for its accessibility and low cost. The pilot results demonstrated high acceptability and a 78.6% retention rate over 16 weeks, highlighting that the culturally sensitive approach and accessible language enabled participants to apply mental health tools autonomously and effectively.
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Research on artificial intelligence (AI) and mental health has focused largely on harms at deployment, including chatbot safety, sycophancy, and AI-associated delusions. Less attention has been paid to a prior question: whether the human-generated text and preference judgments that shape large language models are themselves clinically reliable, particularly when self-report may be distorted. This Viewpoint aims to develop the clinical psychiatric construct of collusion—the uncritical acceptance of an unreliable account—as an analytic lens for AI training and deployment, and to argue that the clinical reliability of training and preference data should be treated as an explicit trustworthy-AI criterion in mental-health–relevant systems. A conceptual synthesis of psychiatry, clinical psychology, and AI safety literature was undertaken. The analysis distinguishes three pipeline layers: pretraining corpora, preference data and posttraining methods, and deployment-time interaction. It maps the clinical construct of collusion against adjacent technical concepts, including sycophancy, reward overoptimization, grounding, refusal training, red-teaming, and live monitoring. The synthesis suggests that collusion-like dynamics are least applicable at the pretraining layer and most applicable at the preference-data and deployment layers, where unassessed user or labeler input can be reinforced without corroboration. Existing mitigations, including data curation, Constitutional AI, reward-model evaluation, grounded generation, refusal training, red-teaming, and postdeployment monitoring, address parts of this problem. However, these approaches are not yet organized around a clinically informed account of when self-report is unreliable. The central novelty is therefore not a generic claim about bias, but the proposal that clinical self-report reliability should be assessed as a distinct data-quality and governance dimension. Trustworthy-AI frameworks for mental-health–relevant applications should incorporate clinical expertise in self-report reliability into preference-data design, red-teaming, and postmarket surveillance. Adding the clinical reliability of training and preference data as an explicit criterion could complement existing technical safeguards while leaving empirical evaluation of clinician involvement as an open research agenda.
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Republicans’ recent tax law targets supplemental Medicaid funds that have increasingly propped up hospitals. The cuts are expected to be even bigger than originally forecast, which almost assuredly will provoke an opposition campaign from hospitals.
Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution.
Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows.
The challenge is that many organisations are often layering AI agents onto existing operations, rather than reimagine the operating model and how work will need to be rewired, explains Prasun Shah, global CTO for workforce consulting and chief AI officer at PwC UK Consulting. “They’re embedding AI employees into what is a human operating model,” layering on AI agents to existing workplace structures when “this is like adding sticky tapes to parts of an operating model that is breaking.”
Doing so may be preventing organizations from unlocking the full value agentic AI offers, creating circumstances where disillusionment can quickly creep in. That full value lies in agents’ capacity to execute entire workflows with limited human input. They can coordinate complex tasks, make independent decisions, adjust to changing conditions, and iterate performance.
In early proving grounds that span customer service, HR, and sales, it’s already estimated that AI agents could accelerate business processes by as much as 30% to 50% and low-value work time by 25% to 40% when deployed at scale. But with this capability comes greater complexity and the need for an enterprise-wide change.
Enterprise agentic AI platform Ema describes this change as agentic business transformation (ABT), a term it coined last year in partnership with HFS Research, in an attempt to plug what it sees as a gap in the existing lexicon about AI agents, and to provide enterprises with a new framework with which to think about their own adoption of the technology.
“None of the existing vocabulary captures the full scope of the change,” explains Ema CEO and founder Surojit Chatterjee. “Digital transformation was about moving from paper to software. AI transformation was about adding artificial intelligence to existing processes. Co-pilot is about AI assisting in various human tasks. But ABT is something categorically different: It’s the integration of AI agents into the fabric of the organization.”
For Shah, the dedicated term (ABT) “helps drive the need to redesign an organization in its entirety: its operating model, its workflows, decision rights, and performance management systems.” He emphasizes that “everything that’s needed to ensure those agents are actually active participants in value creation, rather than just point tools or productivity aids.”
According to Ema, ABT encompasses three core pillars: an organization’s technology stack, its workforce, and the metrics used for success.
The first pillar of ABT is the technology stack. “Your existing tech stack was designed for human-operated, application-centric workflows,” says Chatterjee. “It needs to be reconsidered when the actor is an AI agent operating at machine speed across multiple systems simultaneously.”
As AI agents are integrated into an organization, enterprises will need to pivot from a set of linear processes and steps, to rewiring work in a very different way, explains Shah. That’s because the value in AI agents isn’t as another layer in an existing technology stack but as a connective tissue, he explains, moving between or across layers to coordinate a high-level task or retrieve and interpret data from multiple discrete applications. AI agents can create “a true competitive differentiation for an enterprise” by making decisions based on this capacity to contextualize, he says. “That is where the next battleground will be.”
To build this connective tissue, leaders need to adapt their technology stack to surface higher quality decisions from AI agents, prioritizing access to multiple datasets and applications simultaneously to develop tacit knowledge. “Organizations that make this architectural shift become genuinely more adaptive,” says Chatterjee. “When a new business requirement emerges, you don’t wait six months for a software vendor to build a feature. You configure an AI employee using natural language and connect it to the systems it needs. The time from business to production workflow drops from months to days.”
As AI agents are deployed for more use cases, enterprise leaders must consider what this means for dynamics across their workforce, the second pillar of ABT.
Workforce structures today deviate little from the hierarchical model of the early days of industrialization. To maximize efficiency and scale, processes are standardized, tasks are clearly delineated between strategic business units (SBUs), and employees progress up through an organization based on their capacity to optimize output from teams below them. But with AI agents that can execute, coordinate, and optimize tasks—often without managerial coordination—the lines of that established hierarchy become blurred.
In a workforce that blends AI agents and human employees, managers will be freed up from many execution-based tasks but take on new responsibilities associated with managing hybrid teams. Managers “will need to be able to manage issues around trust, explainability, psychological safety, and even status dynamics” to navigate new tensions that could arise in a hybrid workforce, says Shah.
The impact of agentic AI on existing workforce structures goes far beyond the management layer, too. McKinsey predicts that by 2030, three-quarters of current jobs will require redesign, upskilling, or redeployment, and organizations will need to act swiftly to amend recruitment, retention, and remuneration.
Success metrics are the third and final pillar of ABT.
As AI agents assume greater ownership of core enterprise processes, taking on collaborative roles alongside human employees, traditional workforce metrics that focus on activity or output—such as calls handled or reports filed—no longer make sense.
“When you add AI employees into the workforce, activity metrics become meaningless or actively misleading,” says Chatterjee. “An AI employee can handle a thousand customer interactions in the time it takes a human to handle ten. If you measure success by interactions handled, you’ll conclude the AI is working brilliantly while missing whether any of those interactions actually drove customer satisfaction, retention, or revenue.” To correct this, enterprises must develop a new set of metrics that focus on outcome rather than output. That is, metrics on the broader benefits or changes achieved, rather than individual deliverables.
For example, when one of Ema’s large enterprise customers overhauled its own metrics, switching from tool metrics like cost per query and AI accuracy, to outcomes like the percentage of contracts reviewed without human escalation, the measured ROI from agentic AI tripled within two quarters. The changes meant “this customer stopped building point solutions in high-volume, low-complexity workflows and started deploying AI employees where the outcome value was highest,” says Chatterjee.
Integrating new metrics may also require a complete reconfiguration of reward and talent management processes, as well as accountability and ownership within organizations, points out Shah. In human-AI teams, for example, although ethical and fiduciary responsibilities will likely remain with human employees, operational accountability will become significantly more diffused to reflect the systemic role of AI agents.
This change will raise new questions that senior leadership teams will need to wrestle with, Shah adds. They’ll need to consider: Who is accountable when an AI employee makes a mistake? What happens when AI and humans disagree? What guardrails should be erected to safeguard customers?
Systems-level change is gradual. These are complex lines of inquiry that experts continue to grapple with. But in kickstarting internal dialogue about the core pillars of ABT—the workforce, the technology stack, and the metrics by which success can be gauged—leaders can lay the groundwork for an enterprise better poised to embrace AI agents at a systems level and start to close the gap between their ambition and execution.
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.
WASHINGTON — President Donald Trump had another medical exam on Tuesday, putting his health under renewed public scrutiny as he has worked to dismiss concerns over his age and stamina.
The 79-year-old president spent more than three hours at Walter Reed National Military Medical Center for what the White House described as preventive medical and dental checkups. It was Trump’s fourth publicly disclosed medical exam since he returned to office for a second term, and it comes as he tries to project strength ahead of midterm elections that will test his sway with voters.
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