The accelerating integration of digital technologies with human experience has precipitated profound cognitive, emotional, and behavioral transformations, giving rise to emergent psychopathologies that remain insufficiently addressed by traditional diagnostic taxonomies. This study introduces a novel reconceptualization of mental health in the digital era, delineating four original diagnostic categories: Cognitive Fragmentation and Digital Overload Disorders, Social Media and Immersive Technology-Induced Disorders, Technology Integration Disorders, and Symptom-Driven Disorders Requiring Diagnostic Adaptation. To explore the clinical salience of these emerging constructs, a mixed-methods pilot investigation was conducted involving a cross-sectional survey of 75 licensed mental health professionals and a retrospective analysis of 225 anonymized patient records. Findings revealed substantial clinical recognition of digital-era syndromes, with Continuous Partial Attention Disorder (CPAD) and Digital Anxiety Disorder (DAD) endorsed by 85.3% (n = 64/75) and 82.7% (n = 62/75) of clinicians, respectively. Symptom severity was predominantly rated as moderate. Inferential analyses revealed a statistically significant association between years of clinical experience and recognition of AI-related psychopathologies, including AI Identity Diffusion Disorder [χ2 (1, N = 75) = 5.33, p = .021]. Chart review corroborated these findings, with 76% (n = 171/225) of cases documenting symptoms consistent with digital-era psychopathologies, and CPAD alone noted in 36.4% of records. These results underscore the growing clinical relevance of technology-induced mental health disorders and highlight the urgent need to evolve current diagnostic frameworks. The paper calls for the development of standardized, digitally responsive assessment tools and the design of innovative, context-sensitive therapeutic modalities. Future research should prioritize longitudinal investigations, digital phenotyping, and psychometric validation to enhance diagnostic precision and treatment effectiveness in an increasingly digitized world.
A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction
The exponential growth of biomedical data necessitates advanced tools for efficient information extraction (IE) to support clinical decision-making and research. Large language models (LLMs) have