HistoDiffusion++: A generative visual foundation model for histopathology star
Background Generative artificial intelligence (Gen AI) is becoming increasingly used in mental healthcare. While these systems offer scalable, human-like interactions, concerns persist regarding safety, clinical validity, and the absence of systematic assessment frameworks. Objectives This study systematically reviews existing research on Gen-AI mental-health applications to synthesize current evaluation practices and identify key methodological steps needed for pre-deployment assessment. Methods A systematic search was conducted across major databases (PubMed, IEEE Xplore, and PsycINFO) following PRISMA guidelines. Studies published between 2017 and 2024 examining Gen-AI tools for mental healthcare were included. The final sample of 72 studies was explored to identify assessment domains. Results Four domains of assessment emerged: technical performance, which focuses on model accuracy and reliability on mental-health tasks; clinical validity, which examines alignment with evidence-based guidelines and clinician judgments; expert perceptions, which assess perceived quality and safety of AI-generated outputs; and user perceptions, which capture how end-users experience, interpret, and trust these responses. Conclusion Findings highlight the need for unified, multilayered frameworks to guide the safe and responsible deployment of patient-facing Gen-AI mental-health tools. The review proposes a high-level pre-deployment assessment framework to support researchers, developers, clinicians, and regulators.
Fault zone heterogeneity takes several forms, such as surface roughness, lithological variations, and the intermixing of mineral phases with contrasting frictional properties within the fault core. Each is thought to affect earthquake nucleation and arrest, yet their effects on preparatory processes before failure and on earthquake recurrence remains insufficiently understood. Here, we conducted direct shear experiments on binary mixtures of soda-lime glass beads and quartz powder, systematically varying the gouge composition from 100 to 0 wt% glass beads at 313 +/- 15 kPa normal stress. Frictionally contrasting phases are intermixed at the grain scale, making the mixtures spatially homogeneous but compositionally heterogeneous. Homogeneous glass beads, as one end member of the gouge mixtures, produce quasi-periodic stick-slip with pronounced pre-failure dilation, late-stage rapid slip acceleration, and time and slip predictable recurrence. In contrast, compositionally mixed gouges accelerate more gradually and exhibit shorter dilational phases with small amplitudes. Furthermore, preparatory slip starts earlier in the interseismic period and evolves towards an intermittent critical stress state prior to failure, which is associated with increasing deviation from time-predictable recurrence and a growing fraction of aseismically accommodated slip. The transition from stick-slip to stable sliding occurs within a narrow compositional range (80-85 wt% glass beads), with peak slip velocities differing by approximately 3.8 orders of magnitude between the two regimes. These results demonstrate that gouge composition strongly affects preparatory processes, and predictability of stick-slip recurrence in the laboratory and potentially in nature.
