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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When AI Colludes: Clinical Reliability of Training and Preference Data as a Trustworthy-AI Criterion
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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STAT+: $775 billion, $1.2 billion, and $38k
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Hello, diligent HCI readers! I hope everyone enjoyed their Memorial Day weekends. We’ve got a lot of numbers in today’s edition. Get out your abacus. And tell me if you want more or less math in here: bob.herman@statnews.com.
$775 billion
Centers for Medicare and Medicaid Services
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.
GPS Tracking Upends Rural Uganda’s Schistosomiasis Transmission Assumptions
In Uganda’s fishing villages along lakes and rivers, wearable GPS devices are offering a new, detailed picture of how schistosomiasis spreads, helping refine control strategies for a disease affecting about 250 million people globally, mostly in rural sub-Saharan Africa.
A Nature Health study by researchers at the University of Oxford’s Big Data Institute shows that simple models using GPS-tracked movement can accurately predict which open-water sites people use, how often they visit them, and which sites are most likely to drive transmission of Schistosoma mansoni, the parasite that causes schistosomiasis.
“Snail fever”
Schistosomiasis, or “snail fever,” is caused by Schistosoma mansoni flatworms that parasitize freshwater snails. Infection occurs when people contact contaminated water where larval forms of the flatworm penetrate the skin. Repeated exposure leads to reinfection and chronic disease, including liver damage, portal hypertension, bladder fibrosis, kidney damage, and increased cancer risk.
While praziquantel can cure infection, mass drug administration (MDA) with the antiparasitic medication has failed to interrupt transmission of the parasite. The World Health Organization (WHO) notes that MDA alone is insufficient, since transmission persists in localized hotspots. Focal interventions are needed, but researchers have limited them due to poor data on where water contact actually occurs.
Conventional assumptions have limited modeling of human contact with open-water sites. Though open-water contact is heterogeneous within villages and households, it is often assumed that people use only the site closest to their household or village. However, how mobility affects site usage patterns and whether assignment rules beyond nearest distance can be more realistic are unclear.
Along with praziquantel MDA, the 2022 WHO schistosomiasis control guidelines recommend safe water, sanitation, and hygiene (WASH) as the main intervention, but evidence is mixed. Lack of reliable data has made it difficult to determine why the intervention did not improve biannual MDA. Most water contact and WASH studies use self-reported data or household distance to sites and taps, which lacks objective, spatially granular data to characterize fine-scale water usage patterns and quantify WASH’s impact on water contact and (re)infection.
Focal exposure
Lead author Fabian Reitzug, PhD, and colleagues from Goylette F. Chami’s lab tracked 452 people using GPS loggers in three Ugandan districts for 10 days. A total of 8,200 water contact events occurred at 69 open-water sites and 32 improved sources like taps and boreholes—deep drilling to groundwater. Of the participants, 63.9% used open water and 33.2% improved sources.
Reitzug and colleagues found, unsurprisingly, that distance strongly predicted behavior: usage dropped sharply with distance. Open-water contact was ~70% at 20 meters from home and 11% at 500 meters. Nearly all tap and borehole use occurred within 1 km of home, and over 99.5% of open-water contact occurred within 3 km, showing highly localized exposure.
Adding mobility metrics (such as “radius of gyration”) did not improve predictions. This simple finding challenges the assumption that mobile phone tracking can reliably estimate infection risk. Schistosomiasis exposure appears to be caused by local, routine movements, not long-distance travel.
The study found little evidence that safe water infrastructure reduces risky water contact. Taps and boreholes rarely replaced open-water use; fewer than 2% of people fully substituted safe water for natural sources. Daily activities like bathing, fishing, and washing still require lake or river contact. Behavior varied by location. In the Western Ugandan district of Buliisa, nearly 90% visited open water, compared with 44% in the Eastern Ugandan district of Mayuge.
When incorporated into transmission models, GPS-informed movement patterns closely reproduced observed reinfection rates. Simple “nearest-water-source” assumptions overestimated risk. The improved model also identified high-risk water sites by combining human use with ecological suitability for snail habitats.
Targeted interventions at key sites
These findings suggest control programs could shift toward targeted interventions at key transmission sites, such as focal snail control, environmental modification, or localized treatment. The study also indicates that a 1 km intervention radius may be more realistic than the current 500 m guideline. Importantly, reliable spatial patterns emerged from as few as 15 participants over 10 days, suggesting the approach is scalable. Key limitations include using proximity as a proxy for water contact and limited seasonal coverage.
Overall, the study reframes schistosomiasis transmission as a highly local, measurable process, enabling more precise, data-driven control strategies. Future research should examine whether similar models apply to other waterborne diseases. Identifying pathogen-specific exposure pathways and collecting GPS logger data from various locations could test this approach’s generalizability.
The post GPS Tracking Upends Rural Uganda’s Schistosomiasis Transmission Assumptions appeared first on Inside Precision Medicine.
MOSAIC: Multimodal In Vivo Imaging Data Powers AI Models for Living Systems
In a new study published in Nature Methods titled, “A multimodal adaptive optical microscope for in vivo imaging from molecules to organisms,” researchers from University of California, Berkeley present high-powered microscopes that can track the development of live specimens, including cell movement through tissue, the evolution of internal cellular structures, and shuttling of proteins and other molecules within the cell. The system, named Multimodal Optical Scope with Adaptive Imaging Correction (MOSAIC), has been implemented in more than a dozen worldwide labs over the past six years.
“Life has to be studied in living tissue, holistically, and over fast timescales and for long periods of time,” said Eric Betzig, PhD, professor of molecular and cell biology at UC Berkeley, 2014 Nobel Prize in Chemistry, and co-corresponding author on the study. “You can’t study something as complex as a cell or organism just by looking at the parts individually—there are something like 40 million protein molecules alone of 20,000 different types.”
The microscope uses a large “vision” language model (LVLM), similar to ChatGPT, to measure petabytes of data, the equivalent of about 500 billion pages of text.
Betzig, who is also a Howard Hughes Medical Institute (HHMI) investigator, refers to the imaging data as five-dimensional (5D) composed of three spatial dimensions, plus time and color. The color comes from fluorescent labels that allow scientists to track multiple subcellular structures simultaneously, such as organelles, membranes, the cytoskeleton and more, as they migrate, change shape, divide and interact over time.
In one video, the authors capture a zebrafish regrowing its tail fin. The video revealed tiny events inside living tissue that are normally difficult to visualize, such as cells near the wound releasing small communication packets, microscopic fibers beneath the skin shifting as the tissue repaired itself, two repair cells fusing together and a red blood cell briefly getting trapped as new blood vessels were remodeled.
Ian Swinburne, PhD, assistant professor of molecular and cell biology at UC Berkeley and collaborator on the work, emphasizes that there’s a wealth of information in these large movies across scales, but it can be difficult for a very well-trained biologist to interrogate the data.
“AI can help us interface with the data and ask or answer questions more easily. Like, ‘How many macrophages are crawling into my tissue during an infection?’ or ‘Can I predict when a cell’s going to start leaving its organ?’ That happens in development but also in cancer during metastasis,” said Swinburne.
Building an LVLM or AI that can handle petabytes of imaging data is a main focus of Berkeley’s Advanced Bioimaging Center, which hopes to create a first-of-its-kind Cell Observatory.
“The impact of MOSAIC will be minimal until we build an AI model that can deal with the data that comes out of those systems. We basically have a gold mine, but we have no ability to get the gold out,” said Srigokul “Gokul” Upadhyayula, PhD, assistant professor in residence of cell biology, development and physiology at UC Berkeley. “The primary output of our Cell Observatory Initiative will be an AI mind that’s able to be our scientific partner in extracting these observations.”
The post MOSAIC: Multimodal <i>In Vivo</i> Imaging Data Powers AI Models for Living Systems appeared first on GEN – Genetic Engineering and Biotechnology News.
Rethinking organizational design in the age of agentic AI
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 sticky tape problem
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.
Growing the AI vocabulary
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.
AI agents as connective tissue
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.”
The workforce, redesigned
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.
From output to outcome
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?
Laying the groundwork for systems-level change
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.
Trump wraps up three-hour medical visit to Walter Reed and declares ‘Everything checked out PERFECTLY’
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.
What do early data from Utah’s Doctronic AI pilot show?
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Today: Eli Lilly’s gene-editing data seems promising for high cholesterol, an AI drug discovery CEO dispelled some AI drug discovery hype, and the new interim FDA chief is so far well received.
Fixing patient flow in healthcare
Alcidion’s Paul Deffley and Tracy McClelland join Jordan Sollof to discuss why patient flow is essential for the NHS and patients.