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    Randomized evidence on AI-enabled personalized treatment planning for adults with depressive-anxiety Spectrum disorders: A systematic review and Meta-analysis
    (Elsevier, 2026) Yildiz, Erman; Yildiz, Erhan
    Background: AI-enabled personalized treatment planning may improve outcomes by tailoring care, yet its clinical impact across modalities remains uncertain. Methods: We preregistered a protocol (PROSPERO CRD420251106013), searched five databases, and included randomized trials comparing AI-enabled personalized planning (adaptive chatbots, clinician-facing CDSS/ stratified care, individualized treatment rules [ITRs]) vs controls. Primary outcome was continuous symptom severity (Hedges' g; negative favors AI) at the longest follow-up using random-effects with Knapp-Hartung adjustment. Dichotomous response/remission was pooled as log OR. Results: At prespecified longest follow-up, AI-enabled personalization reduced depressive symptoms (g =-0.35, 95% CI-0.63 to-0.08; k = 7; N = 757; I2 = 25.2%; tau 2 = 0.0195; 95% PI-0.79 to 0.09) but not anxiety (g =-0.08, 95% CI-0.45 to 0.29; k = 7; N = 875; I2 = 46.2%; tau 2 = 0.0548; 95% PI-0.76 to 0.61). Categorical outcomes favored AI (log OR = 0.35, 95% CI 0.01 to 0.69; OR approximate to 1.42; k = 4; N = 4398; I2 approximate to 0%). Effects tended to be larger vs passive comparators; evidence for clinician-facing CDSS and prospective ITR-guided allocation remains limited. Conclusions: Patient-facing chatbots appear effective as adjuncts for depression but lack efficacy for anxiety, likely due to transdiagnostic limitations. Clinician-facing ITRs show promise for treatment matching but lack robust prospective validation. Notably, most trials pre-date Large Language Models (LLMs) (pre-2024); thus, conclusions regarding LLM-facilitated psychotherapy remain premature. Future trials should evaluate prospective model-guided allocation against active controls, include 6-12-month follow-up, and report variance statistics that enable synthesis.