Background/Objectives: Constitutive activation of the PI3K/AKT/mTOR signaling cascade underlies the aggressive phenotype of fibroblast-like synoviocytes (FLSs) in rheumatoid arthritis (RA); however, a quantitative synthesis of in vitro data on pathway inhibition remains lacking. This systematic review and meta-analysis aimed to (i) aggregate standardized effects of pathway inhibitors on proliferation, apoptosis, migration/invasion, IL-6/IL-8 secretion, p-AKT, and LC3; (ii) assess heterogeneity and identify key moderators of variability, including stimulus type, cell source, and inhibitor class. Methods: PubMed, Europe PMC, and the Cochrane Library were searched up to 18 May 2025 (PROSPERO CRD420251058185). Twenty of 2684 screened records met eligibility. Two reviewers independently extracted data and assessed study quality with SciRAP. Standardized mean differences (Hedges g) were pooled using a Sidik–Jonkman random-effects model with Hartung–Knapp confidence intervals. Heterogeneity (τ², I²), 95% prediction intervals, and meta-regression by cell type were calculated; robustness was tested with REML-HK, leave-one-out, and Baujat diagnostics. Results: PI3K/AKT/mTOR inhibition markedly reduced proliferation (to –5.1 SD), IL-6 (–11.1 SD), and IL-8 (–6.5 SD) while increasing apoptosis (+2.7 SD). Fourteen of seventeen outcome clusters showed large effects (|g| ≥ 0.8), with low–moderate heterogeneity (I² ≤ 35% in 11 clusters). Prediction intervals crossed zero only in small k-groups; sensitivity analyses shifted pooled estimates by ≤0.05 SD. p-AKT and p-mTOR consistently reflected functional changes and emerged as reliable pharmacodynamic markers. Conclusions: Targeted blockade of PI3K/AKT/mTOR robustly suppresses the proliferative and inflammatory phenotype of RA-FLSs, reaffirming this axis as a therapeutic target. The stability of estimates across multiple analytic scenarios enhances confidence in these findings and highlights p-AKT and p-mTOR as translational response markers. The present synthesis provides a quantitative basis for personalized dual-PI3K/mTOR strategies and supports the adoption of standardized long-term preclinical protocols.

Background/Objectives: Artificial intelligence is rapidly permeating the field of psychiatry. It offers novel avenues for the diagnosis, treatment, and prediction of mental health disorders. This structured review aims to consolidate current approaches to the application of AI in telepsychiatry. In addition, it evaluates their technological maturity, clinical utility, and ethical–legal robustness. Methods: A systematic search was conducted across the PubMed, Scopus, and Google Scholar databases for the period spanning 2015 to 2025. The selection and analysis processes adhered to the PRISMA 2020 guidelines. The final synthesis included 44 publications, among which 14 were empirical studies encompassing a broad spectrum of algorithmic approaches—ranging from neural networks and natural language processing (NLP) to multimodal architectures. Results: The review revealed a wide array of AI applications in telepsychiatry, encompassing automated diagnostics, therapeutic support, predictive modeling, and risk stratification. The most actively employed techniques include natural language and speech processing, multimodal analysis, and advanced forecasting models. However, significant barriers to implementation persist—ethical (threats to autonomy and risks of algorithmic bias), technological (limited generalizability and a lack of explainability), and legal (ambiguous accountability and weak regulatory frameworks). Conclusions: This review underscores a growing disconnect between the rapid evolution of AI technologies and the institutional maturity of tools suitable for scalable clinical integration. Despite notable technological advances, the clinical adoption of AI in telepsychiatry remains limited. The analysis identifies persistent methodological gaps and systemic barriers that demand coordinated efforts across research, technical, and regulatory communities. It also outlines key directions for future empirical studies and interdisciplinary development of implementation standards.