The overall observed prevalence estimate was similar to estimates calculated using Bayesian hierarchical and random effects models, and ASD prevalence was lower among non-Hispanic White (White) children than among Asian or Pacific Islander (A/PI) children (27.7) than among Asian or Pacific Islander (A/PI) children (38.5).
A randomized controlled trial testing an expert–fine-tuned Gen-AI–powered chatbot, Therabot, for mental health treatment of adults with clinically significant symptoms of major depressive disorder, generalized anxiety disorder, or at clinically high risk for feeding and eating disorders.
A computational model called Centaur, developed by fine-tuning a language model on a huge dataset called Psych-101, can predict and simulate human nature in experiments expressible in natural language, even in previously unseen situations.
This work explores how increasingly capable AI agents may generate the perception of deeper relationships with users, especially as AI becomes more personalised and agentic.
Proposed models for the neural circuitry of normal anxiety as well as the anxiety disorders are discussed.
This scoping review comprehensively covered ethical aspects of CAI in mental health care including evaluating the risks and benefits of CAI in comparison to human therapists, determining its appropriate roles in therapeutic contexts and its impact on care access, and addressing accountability.
Artificial intelligence tools appeared to be accurate in detecting, classifying, and predicting the risk of mental health conditions as well as predicting treatment response and monitoring the ongoing prognosis of mental health disorders.
Techniques and methods to make AI models more interpretable and understandable to humans including their strengths and weaknesses are presented to demonstrate promising advancements in model interpretability, facilitating better comprehension of complex AI systems by humans.
This position paper argues that the promise of LLM social simulations can be achieved by addressing five tractable challenges, and identifies promising directions, including context-rich prompting and fine-tuning with social science datasets.
It is confirmed that insomnia is a common disorder with a prevalence of 12.4 as the most accurate estimate and the need for standardised ways of assessing insomnia shows the need for standardised ways of assessing insomnia.
Five underlying genomic factors are identified and characterized that explained the majority of the genetic variance of the individual disorders and were associated with 238 pleiotropic loci, which may inform a more neurobiologically valid psychiatric nosology and implicate targets for therapeutic development designed to treat commonly occurring comorbid presentations.
The role of neuroinflammation in depression is explored, focusing on glial cell activation, cytokine signaling, blood–brain barrier dysfunction, and disruptions in neurotransmitter systems, to highlight how inflammatory mediators influence brain regions implicated in mood regulation.
A theoretical investigation of mechanisms by which ultraviolet circular polarisation may be produced in star formation regions, and how dichroic extinction may play a key role in producing an enantiomeric excess.
This work finds that AI companions successfully alleviate loneliness on par only with interacting with another person, and more than other activities such as watching YouTube videos, and shows that self-disclosure and distraction alone do not explain AI companions’ effectiveness.
An exhaustive umbrella review was conducted to systematically assess the prevalence and determinants of pain, depression, and anxiety among cancer survivors worldwide by analyzing systematic reviews and meta-analyses and found that depression and anxiety prevalence among cancer survivors was 33.16% and 30.55%, respectively, with significantly higher rates during COVID-19 at 43.25% and 52.93%.
This study presents, for the first time in GBD, a quantification of the mean age at the time of suicide death, alongside comprehensive estimates of the burden of suicide throughout the world.
Investigation of the role of the SCAN in PD pathophysiology and treatments revealed that the substantia nigra and all PD DBS targets are selectively connected to the somato-cognitive action network rather than to effector-specific motor regions.
It is argued that social scientists can address many of these limitations of Generative AI by creating open-source infrastructure for research on human behavior, not only to ensure broad access to high-quality research tools, but also because the progress of AI will require deeper understanding of the social forces that guide human behavior.
This umbrella review synthesizes data on the prevalence of mental disorder symptoms among university students worldwide, providing crucial insights for clinicians, policymakers, and stakeholders.
How ChatGPT fosters dependency through key features such as personalised responses, emotional validation, and continuous engagement is explored, highlighting the need for further research into the psychological and social impacts of prolonged interaction with AI tools like ChatGPT.
This study investigates people’s trust in and acceptance of AI across health care use cases and demonstrates the potential of using predictive AI models as decision-making tools for implementing and interacting with clients in health care AI applications.
A roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy is provided and a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.
The necessity for longitudinal studies to explore the long-term effects of AI on educational outcomes and mental health is suggested and the importance of incorporating student perspectives for a thorough understanding of AI’s role in education is underscored.
The results can guide various stakeholders—policymakers, practitioners, researchers, educators, and parents or caregivers—in addressing the pervasive impact of brain rot and promoting a balanced approach to technology use that fosters cognitive resilience among adolescents and young adults.
A social penalty for AI use is revealed: Individuals who use AI tools face negative judgments about their competence and motivation from others, creating a paradox where productivity-enhancing AI tools can simultaneously improve performance and damage one’s professional reputation.
This work finds that state-of-the-art VLMs are strongly biased on standard, objective visual tasks of counting and identification, and presents an interesting failure mode in VLMs and a human-supervised automated framework for testing VLM biases.
Key recommendations include a focus on metabolic health from treatment initiation, timely assessment and management of non-response, symptom domain-specific interventions, mitigation of side-effects, and the prompt use of clozapine in cases of treatment resistance.
This paper critically examines emerging connections between mental health and lifestyle factors such as sleep, diet, and exercise and underscores the importance of preventive approaches, promoting mental health literacy, reducing stigma, and fostering resilience through mindfulness, cognitive behavioral techniques, and social support systems.
A systematic review of the literature on augmentation strategies for major depression found no evidence of clinical efficacy as measured by response in augmentation with buspirone, testosterone, methylphenidate, yohimbine, inositol, and atomoxetine and future research will need to investigate optimal duration of augmentation therapy.
Several key domains that may be relevant to the characterization of the individual patient with a FED aimed at personalization of management are reviewed, including symptom profile, clinical subtypes, severity, clinical staging, physical complications and consequences, antecedent and concomitant psychiatric conditions and neurobiological markers.
The proposed Human-Centric Cybersecurity Framework integrates psychological resilience, adaptive training, socio-technical approach, and ethical AI principles, and outlines practical strategies to enhance cybersecurity awareness, reduce vulnerabilities, and promote organizational compliance.
The findings suggest that clinicians should provide anticipatory guidance regarding social media use for young adolescents and their parents using longitudinal, cross-lagged structural equation panel models.
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