TY - JOUR
T1 - Artificial Intelligence–Based Psychotherapeutic Intervention on Psychological Outcomes
T2 - A Meta-Analysis and Meta-Regression
AU - Lau, Ying
AU - Ang, Wei How Darryl
AU - Ang, Wen Wei
AU - Pang, Patrick Cheong Iao
AU - Wong, Sai Ho
AU - Chan, Kin Sun
N1 - Publisher Copyright:
Copyright © 2025 Ying Lau et al. Depression and Anxiety published by John Wiley & Sons Ltd.
PY - 2025
Y1 - 2025
N2 - Background: Artificial intelligence (AI)–based psychotherapeutic interventions may bring a new and viable approach to expanding psychiatric care. However, evidence of their effectiveness remains scarce. We evaluated the efficacy of AI-based psychotherapeutic interventions on depressive, anxiety, and stress symptoms at postintervention and follow-up assessments. Methods: A three-step comprehensive search via nine electronic databases (PubMed, Embase, CINAHL, Cochrane Library, Scopus, IEEE Xplore, Web of Science, PsycINFO, and ProQuest Dissertations and Theses) was performed. Results: Thirty randomized controlled trials (RCTs) in 31 publications involving 6100 participants from nine countries were included. The majority (79.1%) of trials with intention-to-treat analysis but less than half (48.6%) of trials with perprotocol analysis were graded as low risk. Meta-analyses showed that interventions significantly reduced depressive symptoms at the postintervention assessment (t = −4.40, p = 0.001) with medium effect size (g = −0.54, 95% CI: −0.79 to −0.29) and at 6–12 months of assessment (t = −3.14, p < 0.016) with small effect size (g = −0.23, 95% CI: −0.40 to −0.06) in comparison with comparators. Our subgroup analyses revealed that the depressed participants had a significantly larger effect size in reducing depressive symptoms than participants with stress and other conditions. At postintervention and follow-up assessments, we discovered that AI-based psychotherapeutic interventions did not significantly alter anxiety, stress, and the total scores of depressive, anxiety, and stress symptoms in comparison to comparators. The random-effects univariate meta-regression did not identify any significant covariates for depressive and anxiety symptoms at postintervention. The certainty of evidence ranged between moderate and very low. Conclusions: AI-based psychotherapeutic interventions can be used in addition to usual treatments for reducing depressive symptoms. Well-designed RCTs with long-term follow-up data are warranted. Trial Registration: CRD42022330228.
AB - Background: Artificial intelligence (AI)–based psychotherapeutic interventions may bring a new and viable approach to expanding psychiatric care. However, evidence of their effectiveness remains scarce. We evaluated the efficacy of AI-based psychotherapeutic interventions on depressive, anxiety, and stress symptoms at postintervention and follow-up assessments. Methods: A three-step comprehensive search via nine electronic databases (PubMed, Embase, CINAHL, Cochrane Library, Scopus, IEEE Xplore, Web of Science, PsycINFO, and ProQuest Dissertations and Theses) was performed. Results: Thirty randomized controlled trials (RCTs) in 31 publications involving 6100 participants from nine countries were included. The majority (79.1%) of trials with intention-to-treat analysis but less than half (48.6%) of trials with perprotocol analysis were graded as low risk. Meta-analyses showed that interventions significantly reduced depressive symptoms at the postintervention assessment (t = −4.40, p = 0.001) with medium effect size (g = −0.54, 95% CI: −0.79 to −0.29) and at 6–12 months of assessment (t = −3.14, p < 0.016) with small effect size (g = −0.23, 95% CI: −0.40 to −0.06) in comparison with comparators. Our subgroup analyses revealed that the depressed participants had a significantly larger effect size in reducing depressive symptoms than participants with stress and other conditions. At postintervention and follow-up assessments, we discovered that AI-based psychotherapeutic interventions did not significantly alter anxiety, stress, and the total scores of depressive, anxiety, and stress symptoms in comparison to comparators. The random-effects univariate meta-regression did not identify any significant covariates for depressive and anxiety symptoms at postintervention. The certainty of evidence ranged between moderate and very low. Conclusions: AI-based psychotherapeutic interventions can be used in addition to usual treatments for reducing depressive symptoms. Well-designed RCTs with long-term follow-up data are warranted. Trial Registration: CRD42022330228.
KW - artificial intelligence–based psychotherapeutic intervention
KW - meta-analysis
KW - meta-regression
KW - psychological outcome
UR - http://www.scopus.com/inward/record.url?scp=105000555666&partnerID=8YFLogxK
U2 - 10.1155/da/8930012
DO - 10.1155/da/8930012
M3 - Review article
AN - SCOPUS:105000555666
SN - 1091-4269
VL - 2025
JO - Depression and Anxiety
JF - Depression and Anxiety
IS - 1
M1 - 8930012
ER -