Background: Artificial intelligence–powered conversational agents (ie, chatbots) are increasingly popular outlets for users seeking psychological support, yet little is known about how users experience early-stage prototypes or which therapeutic processes contribute to clinical improvement. A transparent evaluation of emerging chatbot prototypes is needed to clarify if, how, and why artificial intelligence companions work and to guide their continued development. Objective: This mixed methods pilot study evaluated user experience, acceptability, and preliminary clinical signals for an early-stage mental wellness chatbot. We also examined whether baseline symptom severity moderated clinical improvement. Methods: Three sequential cohorts (n=125) completed a 2-week, incentivized chatbot exposure (approximately 60 min per week). Participants provided first-impression ratings, qualitative feedback, and pre–post assessments of depressive symptoms (PHQ-8 [Patient Health Questionnaire-8]), anxiety symptoms (GAD-7 [Generalized Anxiety Disorder-7]), psychological distress, well-being, and loneliness. Statistical models estimated symptom change and tested interactions with baseline symptom severity. Mixed methods analysis integrated quantitative outcomes with large language model–assisted qualitative content analysis of open-ended responses. Results: Participants described the chatbot as accessible, easy to use, and emotionally validating, while citing limitations in personalization and conversational depth. Qualitative responses consistently highlighted early therapeutic processes such as emotional validation, goal setting, and perceived attunement. Regression models showed significant pre–post reductions in depressive (Hedges =–0.32) and anxiety (=–0.32) symptoms, alongside modest improvements in distress and well-being. Baseline severity moderated improvement, with marginal effects indicating larger predicted reductions at higher PHQ-8 and GAD-7 baseline scores (eg, PHQ-8=15: =–0.84; GAD-7=15: =–0.62). Conclusions: This pilot provides a comprehensive view of early chatbot development and suggests promising user experiences and preliminary symptom improvements under structured pilot conditions. By integrating experiential and exploratory clinical data, the study identifies candidate process targets to inform ongoing refinement. Findings support continued development and demonstrate procedural feasibility for progression to larger, longer-term trials evaluating engagement and clinical outcomes under more naturalistic conditions.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/df551c8cc1fc34d8080828a3b50a6924" />
Development of Virtual Mental Health Stepped Care Service for a Heart Failure Remote Management Program: Qualitative Descriptive Study
Background: Depression is highly prevalent yet undertreated among people living with heart failure, indicating barriers to mental health services. Although various digital mental health interventions have been developed to detect, treat, and manage depression in this population, these interventions have seen limited integration into clinical care and a lack of implementation research. Stepped care is a service innovation that may promote the implementation of these technologies into clinical settings, but few studies have examined how these services are designed in clinical settings. Objective: This study aimed to identify strategies to address health system barriers to accessing mental health care from the perspective of people living with heart failure, clinicians, and researchers, and to incorporate these strategies into the design of a virtual mental health stepped care service within a heart failure remote management program. Methods: A qualitative description study was conducted using purposive recruitment of people living with heart failure, clinicians, and researchers from a heart failure remote patient management program. As part of a service design approach, semistructured interviews explored potential strategies to address barriers to accessing mental health services. Two researchers coded the data descriptively and constructed themes to guide the development of a virtual stepped care service. Results: A total of 22 participants were interviewed, comprising 13 people living with heart failure and 9 clinicians and researchers. Six themes were identified, comprising 4 requirements and 2 foundational principles. The requirements were to (1) adopt a collective approach to identify distress across methods, people, and time points; (2) maintain a referral-based approach; (3) rely on existing mental health human resources; and (4) offer patient choice among various mental health care options. These requirements were supported by two principles: (1) building on organizational strengths and (2) reducing treatment burden. Based on these findings, a virtual stepped care service was developed, incorporating a depression screening module, referral-based workflows, and, where clinically appropriate, patient choice in treatment selection. Conclusions: The stakeholder-informed design of this virtual stepped care service contributes to the limited literature on stepped care service design and demonstrates how such models can be tailored to their intended contexts. Although each component was designed to address health system barriers to mental health care for people living with heart failure, resource limitations may constrain the balance between feasibility and quality of care. Future research should evaluate the acceptability of this model among people living with heart failure and clinicians.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/55252764b80ef3ce085b4f3f728aad47" />
Prediction of Relapse Using Digital Technology in People in Recovery From Substance Use Disorders: Early Economic Evaluation With a Case Study of the Subreal App
Background: Many people relapse after achieving abstinence in substance use disorders. Health care providers may scan the horizon for new technologies to predict response that allow interventions to be targeted rather than routine. Currently, no such predictive technologies are available in the United Kingdom. The Subreal app is available for use in research contexts, but no clinical data specific to the app are yet available. Early health economic modeling can use data from the literature to explore characteristics essential for the new technology to be cost-effective. This information can guide developers in setting performance targets and pricing and estimating potential cost savings and/or cost-effectiveness for health care providers. Objective: This study was supported by a UK industry funding body to explore the potential of digital technologies such as the Subreal app to offer cost savings or cost-effectiveness for health care providers. We explored the threshold price and clinical effectiveness required to deliver cost savings and cost-effectiveness in 2 subpopulations with substance use disorders in a UK setting. Methods: Deterministic models were used to estimate costs per relapse and quality-adjusted life years over 1-, 5-, and 20-year time horizons for people who have achieved abstinence after treatment for alcohol or opioid misuse. The intervention was a digital technology predicting relapse, provided—in addition to standard care—for 1 year post achievement of abstinence. In Subreal, biomarker data are collected daily through the app, and artificial intelligence–enhanced risk assessment flags patients who require additional support. The comparator was event-driven, reactive response to relapse. Costs and quality-of-life estimates were calculated using Markov models with data from existing published sources. The base-case estimate of 15% reduction in first-year relapse rates was based on a previous study on a similar but simpler digital technology. Results: Digital technologies such as the Subreal app have the potential to be cost-saving from a UK health and social care perspective, especially when used over a longer time horizon. Assuming a reduction of 15% in first-year relapse rates, digital technologies have the potential to be cost-saving, provided that they do not cost more than £300 (US $400.09) and £460 (US $613.47) per patient per annum for alcohol and opioid use disorders, respectively. No cost was included for postalert care, as it was assumed that this could be met within existing resources. Cost savings would be achieved predominantly through a reduction in treatment requirements as fewer people relapse. Price thresholds would reduce correspondingly if a <15% reduction in relapse rates were achieved. Conclusions: Developers of digital technologies that aim to reduce relapse need to focus on the generation of evidence of clinical effectiveness and develop a commercially sustainable pricing model that allows health care providers to benefit from cost savings.
The Download: the state of AI, and protecting bears with drones
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.
Want to understand the current state of AI? Check out these charts.
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. Stanford’s 2026 AI Index—the field’s annual report card—cuts through the noise.
The data reveals a technology evolving faster than we can manage. From the China-US rivalry and model breakthroughs to public sentiment and the impact on jobs, here are the index’s key findings on the state of AI today.
—Michelle Kim
Why opinion on AI is so divided
Stanford’s 2026 AI Index is full of striking stats. It also reveals a field riddled with inconsistencies, most notably in the gap between experts and non-experts.
On jobs, 73% of US experts view AI’s impact positively, compared to just 23% of the public. Similar divides emerged on the economy and healthcare. What’s driving this disconnect?
Part of the answer may lie in their diverging experiences. Those using AI for coding and technical work see it at its best, while everyone else gets a more mixed bag. The result is two very different realities. Read the full story on what they are—and why they matter.
This story is from The Algorithm, our weekly newsletter on AI. Sign up to receive it in your inbox every Monday.
—Will Douglas Heaven
Job titles of the future: Wildlife first responder
Grizzly bears have made such a comeback across eastern Montana that in 2017, the state hired its first-ever prairie-based grizzly manager: wildlife biologist Wesley Sarmento.
For seven years, Sarmento worked to keep both bears and humans out of trouble. He acted like a first responder, trying to defuse potentially dangerous situations. He even got caught in some himself, which led him to a new wildlife safety tool: drones. Find out the results of his experiments in digital ecology.
—Emily Senkosky
This article is from the next issue of our print magazine, which is all about nature. Subscribe now to read it when it lands on Wednesday, April 22.
The must-reads
I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.
1 Human scientists still trounce the top AI agents at complex tasks
The best agents perform only half as well as experts with PhDs. (Nature)
+ Can AI really help us discover new materials? (MIT Technology Review)
2 OpenAI is escalating its fight with Anthropic while pulling away from Microsoft
A leaked memo exposes plans to attack Anthropic. (Axios)
+ And says Microsoft “limited our ability” to reach clients. (The Information $)
+ While touting a budding alliance with Amazon. (CNBC)
3 Carbon removal technology is stalling—and that may be good news
Better solutions could now emerge. (New Scientist)
+ Here are three that are set to break through. (MIT Technology Review)
4 AI is finding bugs faster than we can fix them—and hackers will benefit
Welcome to the bug armageddon. (WSJ $)
+ AI may soon be capable of fully automated attacks. (MIT Technology Review)
5 A Texas man has been charged with the attempted murder of Sam Altman
He allegedly threw a Molotov cocktail at the OpenAI CEO’s home last Friday. (NPR)
+ The suspect reportedly had a list of other AI leaders. (NYT $)
6 AI is beginning to transform mathematics
It’s proving new results at a rapid pace. (Quanta)
+ One AI startup plans to unearth new mathematical patterns. (MIT Technology Review)
7 Students are turning away from computer science
It’s had a massive drop in enrollments. (WP $)
+ AI coding tools have diminished the degree’s value. (NYT $)
8 India’s bid to become a data center hub is sparking a fierce backlash
Farmers are protesting Delhi’s courtship of hyperscalers. (Rest of World)
9 Meta is set to overtake Google in advertising revenue this year
And become the world’s largest digital ad platform for the first time. (WSJ)
10 AI influencers are taking over Coachella
Synthetic content creators are “everywhere” at the festival. (The Verge)
Quote of the day
“These people are almost nothing like you. They are most likely sociopathic/psychopathic and, in the case of Altman, consistently reported to be a pathological liar.”
—The alleged firebomber of Sam Altman’s home shares his distrust of AI leaders in a blog post.
One More Thing
We’ve never understood how hunger works. That might be about to change.
A few years ago, Brad Lowell, a Harvard University neuroscientist, figured out how to crank the food drive to the maximum. He did it by stimulating neurons in mice. Now, he’s following known parts of the neural hunger circuits into uncharted parts of the brain.
The work could have important implications for public health. More than 1.9 billion adults worldwide are overweight, and more than 650 million are obese. Understanding the circuits involved could shed new light on why these numbers are skyrocketing.
—Adam Piore
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.)
Top image credit: Stephanie Arnett/MIT Technology Review | Getty Images
+ Someone built a mechanical version of Tony Hawk’s Pro Skater from Lego.
+ Enjoy this wholesome clip of toddlers discovering the existence of hugs.
+ This interactive body map shows exactly which exercises you need.
+ Jon McCormack’s photos of nature’s patterns are breathtaking.
Mitochondrial dysfunction, neuroinflammation, and associated mechanisms in sepsis-associated encephalopathy: from pathogenesis to emerging therapeutics
Sepsis-associated encephalopathy (SAE) is a devastating neurological complication of sepsis, leading to diffuse brain dysfunction, long-term cognitive deficits, and increased mortality. Its pathogenesis is complex, with mitochondrial dysfunction and neuroinflammation emerging as central, interconnected drivers. This review systematically elucidates the pathogenic crosstalk between these two processes. We detail how dysregulated mitochondrial dynamics (e.g., Drp1-mediated fission), impaired biogenesis (via the proliferator-activated receptor-gamma coactivator-1α axis), oxidative stress, and the activation of mitochondria-dependent cell death pathways (ferroptosis, pyroptosis) contribute to neuronal injury. Concurrently, microglial activation, particularly through the NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasome, creates a vicious cycle that exacerbates mitochondrial damage and synaptic loss. Furthermore, we summarize emerging therapeutic strategies that target this mitochondrial-neuroinflammatory axis, including molecular hydrogen, mitochondria-targeted peptides (SS-31), natural compounds, and specific inhibitors (e.g., Mdivi-1, MCC950). The integration of recent insights on the gut-brain axis and cerebral metabolomics further expands the therapeutic landscape. Ultimately, targeting this core axis offers a promising paradigm for developing effective interventions to improve neurological outcomes in septic patients.
A longitudinal analysis of the prevalence of restrictive interventions involving women with mental health conditions, learning disabilities or autism in mental health services in England
IntroductionRestrictive interventions, including physical restraint, seclusion, chemical restraint, and segregation, continue to be used within mental health services, despite sustained policy efforts to promote least-restrictive and trauma-informed care. However, little is known about national trends affecting women, for whom restrictive interventions often carry heightened risks of re-traumatisation and stigma.MethodsWe conducted a longitudinal secondary analysis of publicly available administrative data from the Mental Health Bulletin covering NHS-funded mental health services in England between 2017 and 2025. Annual counts of restrictive interventions involving women were examined relative to the number of women detained under the Mental Health Act to estimate annual rates per 1,000 detained. Regression modelling was used to assess temporal trends overall, by age group and type of restrictive intervention, and interrupted time-series analyses to examine changes following implementation of the Mental Health Units (Use of Force) Act 2018 (“Seni’s Law”). Trends were also examined alongside available national data on restrictive interventions involving men.ResultsRates of restrictive interventions involving women increased by approximately 12 percent per year over the study period, with no evidence of a reduction following the introduction of Seni’s Law. Increases were most pronounced for chemical restraint, seclusion, and segregation, while physical and mechanical restraint remained stable. Restrictive interventions declined among women under 18 but increased consistently across all adult age groups, indicating a widening age-related divergence. Although overall trends broadly mirrored those observed among men, the types of restrictive interventions used and their potential impact may differ, highlighting gendered dimensions in how restrictive practices are experienced and applied.DiscussionDespite extensive national initiatives, restrictive interventions involving women have continued to rise in England, highlighting a persistent gap between policy intent and practice. The findings suggest that legislative frameworks alone are insufficient to achieve meaningful reductions without operational changes in clinical practice, organisational culture, and monitoring systems. Internationally, the study contributes rare gender-disaggregated longitudinal evidence and highlights the need for comparable monitoring systems and coordinated research to inform rights-based, trauma-informed strategies to reduce restrictive interventions in mental health services.
Unmasking deep-rooted trauma: long-term effects of childhood adversities on posttraumatic stress disorder in healthcare workers facing acute multi-trauma
PurposeIn recent years, healthcare workers (HCWs) in Lebanon have encountered compounded traumatic exposures, including the Beirut Port blast, COVID-19, and an ongoing economic crisis, often preceded by early-life adversities such as adverse childhood experiences (ACEs). Understanding how these acute stressors interact with early adversities is crucial for assessing their long-term psychological impact. Accordingly, this study examines the extent to which these combined factors predict the development of full and subthreshold posttraumatic stress disorder (PTSD) over time.MethodsA cohort study was conducted following 296 HCWs from Saint George Hospital University Medical Center, with assessments at two timepoints: 6–7 months and 2–2.5 years after the Beirut Blast. PTSD symptoms were measured using the PCL-5, applying both full-threshold criteria and six definitions of subthreshold PTSD. Bivariable and multivariable analysis were conducted.ResultsAt 6–7 months, acute stressors (financial hardship, Beirut Blast, and COVID-19) were significantly associated with PTSD across most definitions. However, by 2–2.5 years, ACEs became the strongest and most consistent predictor of both full-threshold and subthreshold PTSD, while the impact of acute stressors diminished.ConclusionThe impact of acute trauma on the risk of PTSD fades over time, while early-life adversity has an enduring impact. The findings highlight the importance of including developmental trauma histories in PTSD assessments. In concordance with stress sensitization and neurobiological models, the results indicate a marked temporal shift, where the diminishing effects of acute stressors give way to the enduring role of early life adversity in shaping PTSD symptom trajectories.
How stressful life events are associated with depression: the mediating pathway of security in a clinical adolescent sample
BackgroundStressful life events are well-established risk factors for adolescent depression; however, the psychological mechanisms underlying this association remain insufficiently understood, particularly regarding which types of stress and which dimensions of security are most closely linked to depression. This study aimed to investigate whether security and its two sub-dimensions statistically mediated the association between stressful life events and depression among clinically diagnosed adolescents, while also examining the relative strength of indirect associations across specific stress types.MethodsA cross-sectional study was conducted with 284 adolescents (70.1% female; mean age = 15.82 ± 1.86 years) diagnosed with major depressive disorder according to the DSM-5 criteria at a tertiary psychiatric hospital in Western China. Participants completed the Adolescent Self-Rating Life Events Checklist (ASLEC), Self-Rating Depression Scale (SDS), and Security Questionnaire (SQ) questionnaires. Simple mediation, parallel mediation, and dimension-specific analyses were performed using the PROCESS macro (Model 4) with 5,000 bootstrap resamples, controlling for gender and parental marital status.Resultsstressful life events were significantly positively correlated with depression (r = 0.491, p < 0.001) and negatively correlated with security (r = −0.464, p < 0.001). Simple mediation analysis revealed that security demonstrated a significant indirect association through security (indirect effect = 0.176, 95% CI [0.126, 0.232]), accounting for 53.8% of the total association. Parallel mediation analysis further indicated a dual-pathway model: both Interpersonal Security (indirect effect = 0.083, 95% CI [0.037, 0.133]) and Certainty in Control (indirect effect = 0.093, 95% CI [0.043, 0.152]) functioned as significant statistical mediators of comparable magnitude, with no significant difference between them (Contrast = −0.010, 95% CI [−0.065, 0.042]). Furthermore, dimension-specific analyses revealed that Interpersonal Stress (standardized indirect effect = 0.266) and Academic Stress (standardized indirect effect = 0.231) showed the strongest indirect associations with depression through the security pathway. Exploratory subgroup analyses revealed a gender-crossed pattern: for male adolescents (n = 85), the indirect association was significant only through Interpersonal Security (effect = 0.116, 95% CI [0.048, 0.199]); for female adolescents (n = 199), it was significant only through Certainty in Control (effect = 0.136, 95% CI [0.067, 0.212]).ConclusionSecurity functions as a significant statistical mediator in the association between stressful life events and adolescent depression. The findings are consistent with a “dual-pathway” model wherein stress is concurrently associated with lower levels of both relational security (Interpersonal Security) and personal agency (Certainty in Control). Exploratory analyses suggest that the relative importance of these two pathways may differ by gender. If confirmed by future longitudinal research, clinical interventions may benefit from an integrated approach that addresses both dimensions, with particular attention to interpersonal conflicts and academic pressure as the stressors most strongly associated with depression through security pathways.
Medical evaluation of first presentation of psychotic symptoms in children and adolescents
IntroductionPsychotic symptoms in children and adolescents may represent either normative developmental phenomena or severe psychiatric and medical conditions, requiring careful differential diagnosis.MethodsThis retrospective study aimed to evaluate the medical workup of children and adolescents admitted for a first presentation of psychotic symptoms at a tertiary pediatric center over a 10-year period. The sample included 68 patients (mean age 13.7 ± 3.7 years) who underwent clinician-directed evaluations including physical exams, laboratory tests, toxicology screens, neuroimaging, and lumbar puncture when indicated.ResultsSixteen patients (23.5%) were diagnosed with substance-/medication-induced or medically-associated psychosis. In this cohort, younger age, very early onset psychosis (<13 years), and catatonia at first presentation were more frequently observed among patients with secondary etiologies, whereas documented prior subthreshold symptoms were more frequently documented among those diagnosed with primary psychiatric disorders. Most investigations did not identify a secondary cause, reflecting clinician-directed evaluation in routine practice; however, selected cases (e.g., autoimmune encephalitis, multiple sclerosis) illustrate the clinical importance of careful assessment when specific suspicion is present.ConclusionThese findings suggest that targeted medical evaluation may be useful in pediatric psychosis, particularly when clinical features raise suspicion for secondary etiologies, and may help inform clinical decision-making in tertiary pediatric settings.
Self images: an empirical enquiry into Rembrandt’s self-portraits
Many have speculated that events of personal and financial loss in the life of Rembrandt van Rijn (Rembrandt) caused depression and that this is revealed by examination of his work particularly self-portraits painted in old age. Some report detecting various physiological diseases associated with aging, including vision impairment, which may have affected his mood and work. Aging and neurodegenerative disease which often accompanies it, are both associated with depression. Depression is characterised by visual deficits including perception of reduced contrast and colour. Age-related neurological disorders are associated in artists with reduced complexity. Recent advances in imaging and computer technology make it possible to empirically examine changes in artistic style which can contribute to understanding artists’ physical and mental health. Previous studies have identified associations between adverse events in artists’ lives and altered contrast and colour in their self-portraits. In the current study changes in contrast, colour and fractal dimension were measured in the entire corpus of Rembrandt’s painted self-portraits and portraits to determine whether changes in style indicate depression, cognitive decline, or neurological disease and whether differences in style can be detected between self-portraits and portraits of related and unrelated others. Productivity was also examined as an indirect indicator. The results suggest that it is unlikely that Rembrandt suffered from unipolar or bipolar depression, age-related cognitive decline, or neurodegenerative disease. The data are consistent with someone experiencing episodes of low mood associated with normal grieving and adversity followed by resilient recovery. There is evidence of a gradient in saliency and complexity between self-portraits and related and unrelated portraits and of a ‘late’ style identified by leading art historians consistent with macular degeneration.