Public discussion has increasingly focused on violent incidents involving individuals diagnosed with schizophrenia, particularly those who are nonadherent with treatment or are in the early stages of illness before treatment needs are recognized. Although people with serious mental illness are somewhat more likely to commit violent acts than those in the general population, only a small proportion of individuals with schizophrenia do so, and they are far more often victims than perpetrators of violence. Misconceptions linking schizophrenia with violence contribute to stigma, delay early diagnosis and intervention, and divert attention from contributing factors such as substance use disorders. While structured assessment tools exist, precise methods for identifying those at highest risk for committing a violent act remain limited. Early recognition of the prodromal phase of schizophrenia, combined with timely pharmacological and psychosocial interventions, can meaningfully reduce the risk of violence. Ongoing research should emphasize improving predictive tools and promoting effective prevention and treatment strategies.

Rapid urbanization and the algorithmically mediated digital environment have been linked to a "digital pandemic" in youth mental health. As Generation Z transitions from play-based to phone-based childhoods, understanding how digital architecture interacts with urban stressors is critical. This review delineates the socio-neurobiological mechanisms underlying this crisis and proposes a comprehensive multi-tiered public health framework for technology-led intervention. Emerging evidence suggests that attention-optimizing algorithms exploit neurodevelopmental vulnerabilities, intensifying negative affect and maladaptive social comparison. Recent studies link digital immersion to circadian disruption, hypothalamic-pituitary-adrenal axis dysregulation, and systemic low-grade inflammation. Urban stressors - including sensory overload and reduced green space - further sensitize the "social brain," creating an evolutionary mismatch that amplifies algorithmic influence and psychological distress. We define the "digital pandemic" as a population-level phenomenon associated with algorithmic pathogenesis, and propose a "Digital Precision Psychiatry" framework that shifts the clinical paradigm from episodic, subjective observation to continuous, objective management. By utilizing digital phenotyping and Just-in-Time Adaptive Interventions (JITAI), this vertically integrated strategy aims to restore bio-psycho-social resilience in youth, turning the digital environment from a source of pathology into a tool for neuroprotection.

Artificial intelligence is increasingly advancing both fundamental research and clinical applications in schizophrenia. This review surveys recent literature on artificial intelligence driven approaches for schizophrenia diagnosis, treatment, management, and characterization, using multiple data modalities such as neuroimaging, electrophysiology, electronic health records, and genomic data. Recent work shows substantial progress in leveraging machine learning and deep learning for diagnostic label prediction, treatment response modeling, and brain network characterization. While many studies continue to improve feature extraction and classification methods within single modalities, there is a growing trend to utilize multiple data sources to capture the complexity of schizophrenia from a comprehensive perspective. Emerging themes include multimodal fusion methodologies to identify linked correlates of schizophrenia, as well as data-driven approaches to learn subgroups, brain networks, and psychosis continua. The rise of large-scale multimodal datasets, foundation models, and mechanistic interpretability methods holds promise for scalable symptom assessment and biomarker identification, thereby better supporting early intervention and personalized treatment. Current literature highlights a shift from unimodal prediction to holistic, multimodal characterization of schizophrenia. Transforming these artificial intelligence models into clinical tools, however, requires careful attention to patient privacy and data bias, alongside rigorous validation across diverse populations and settings.

Addictive behaviors, including both substance use disorders and behavioral addictions, arise from complex interactions among biological, psychological, social, and environmental factors including digital ones. This review focuses on the assessment of social and psychological risk and protective factors, highlighting how artificial intelligence and machine learning approaches complement conventional qualitative and quantitative methodologies. The aim is to clarify how these tools can enhance understanding, prediction, and prevention of addictive behaviors. Recent research identifies impulsivity, emotion dysregulation, peer norms, and family functioning as central psychosocial risk factors for addictive behaviors. Protective factors - such as self-efficacy, social support, and family cohesion - moderate these risks. Conventional analyses provide foundational evidence, while ML methods (predictive machine learning, explainable artificial intelligence, reinforcement learning) now enable integration of multimodal data, detection of nonlinear patterns, and identification of latent psychosocial profiles. Emerging studies demonstrate potential for early-warning prediction and personalized intervention design. AI/ML offers unprecedented opportunities to advance addiction science by handling high-dimensional psychosocial and behavioral data. Yet, ethical, interpretative, and causal challenges persist. The most promising path forward lies in synergizing theory-driven analytics with data-driven AI approaches to achieve more precise and contextually grounded prevention and intervention strategies for addictive behaviors.

Bipolar disorder (BD) in older adults presents unique clinical challenges, and nonpharmacological interventions are an important component of comprehensive care, with more favorable side-effect profiles. This review synthesizes recent evidence on lifestyle and psychotherapy interventions for older adults with BD. Evidence derives predominantly from observational studies, with few interventional trials. Lifestyle analysis - largely from large-scale biobank cohorts - identifies smoking, sleep dysregulation, sedentary behavior, suboptimal diet, and weight extremes as modifiable risk factors associated with BD onset and course. Despite guideline recommendations, cognitive behavioral therapy for insomnia remains underutilized in BD. In psychotherapy, recovery-oriented and strengths-based models, including Recovery-focused Therapy and Functional Remediation for older adults, show feasibility and acceptability, with a randomized trial underway. Physical activity research is severely limited by absence of interventional studies in older adults with BD, with available evidence indicating that cognitive function and sedentary behavior may be bidirectionally associated. Lifestyle and psychotherapy interventions for older adults with BD remain limited by paucity of interventional evidence, particularly for this age group. Integrated, age-sensitive, and recovery-oriented approaches hold promise. Future research must prioritize trials that include older adults and address lifestyle factors and their impact on course, cognition and psychosocial functioning.

Large language models (LLMs) are increasingly integrated into digital mental health tools, yet their role in substance use disorder (SUD) interventions remains poorly understood. This review synthesizes emerging evidence on the opportunities and risks of applying LLMs across the digital SUD care continuum. Studies report promising applications in early detection, personalized support, continuous monitoring, and relapse prevention. LLMs demonstrate capacity to extract substance-use signals from natural language, generate supportive and motivational responses, and interpret narrative data for patient-reported outcomes. However, risks are substantial. LLMs can produce inaccurate or hallucinated content, may reinforce stigma or demographic bias, and can generate misleading or potentially unsafe advice. Privacy concerns are amplified in SUD contexts, where sensitive data are often managed outside regulated healthcare systems. Existing regulatory frameworks such as the EU AI Act or U.S. device regulations, do not yet provide clear governance for anonymous, AI-supported SUD interventions. LLMs have potential to expand scalable, low-threshold support for SUDs, but their safe deployment requires validation, bias mitigation, transparent data governance, and robust human oversight. Evidence remains preliminary, and clinical integration should proceed cautiously.

With an estimated 41.1B digital devices, the term "digital biomarkers" has been increasingly bandied about in the research literature. There is, however, a significant disconnect between the presumption of digital biomarkers and the reality of digital biomarkers. The research literature embraces the concept of digital biomarkers without concomitant evidence for validation of digital measures as biomarkers. Unlike imaging or blood-based biomarkers, there is a woeful lack of research dedicated to validating digital measures as biomarkers. This gap also presents an opportunity. Regulatory agencies worldwide have long-standing protocols used by pharmaceutical and biotech companies to stand up quality management systems (QMS) that track research from inception to regulatory approved submissions. The recent United States (US) Food and Drug Administration (FDA) approval of Alzheimer's disease (AD) plasma biomarkers is another example where successful QMS implementation provided the processes and transparency necessary to obtain approval. Regulatory guidelines for digital technology validation are more circumspect on validation pathways of AD digital biomarkers, but FDA provides a framework for building a QMS that could potentially do so. Building an open source QMS for AD digital biomarker validation will be a critical breakthrough for harnessing the potential of digital technologies for detection, monitoring and treatment of AD and related disorders.