Journal of affective disorders · 2026
Randomized evidence on AI-enabled personalized treatment planning for adults with depressive-anxiety Spectrum disorders: A systematic review and Meta-analysis.
СтатьяСистематический обзорМетаанализНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Yıldız Erman, Yıldız Erhan
Аннотация
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%; τ2 = 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%; τ2 = 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 ≈ 1.42; k = 4; N = 4398; I2 ≈ 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.
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%; τ2 = 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%; τ2 = 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 ≈ 1.42; k = 4; N = 4398; I2 ≈ 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.