Background: There has been increasing interest in the use of artificial intelligence (AI)-enabled clinical decision support systems (CDSS) for the personalization of major depressive disorder (MDD) treatment selection and management, but clinical studies are lacking. We tested whether a CDSS that combines an AI which predicts remission probabilities for individual antidepressants and a clinical algorithm based on treatment can improve MDD outcomes.

Methods: This was a multicenter, cluster randomized, patient-and-rater blinded and clinician-partially-blinded, active-controlled trial that recruited outpatient adults with moderate or greater severity MDD. All patients had access to a patient portal to complete questionnaires. Clinicians in the active group had access to the CDSS; clinicians in the active-control group received patient questionnaires; both groups received guideline training. Primary outcome was remission (<11 points on the Montgomery-Asberg Depression Rating Scale [MADRS]) at study exit.

Results: Forty-seven clinicians were recruited at 9 sites. Of 74 eligible patients, 61 patients completed a postbaseline MADRS and were analyzed. There were no differences in baseline MADRS (P = .153). There were more remitters in the active (n = 12, 28.6%) than in the active-control (0%) group (P = .012, Fisher’s exact). Of 3 serious adverse events, none were caused by the CDSS. Speed of improvement was higher in the active than the control group (1.26 vs 0.37, P = .03).

Conclusions: While limited by sample size and the lack of primary care clinicians, these results demonstrate preliminary evidence that longitudinal use of an AI-CDSS can improve outcomes in moderate and greater severity MDD.

Trial Registration: ClinicalTrials.gov identifier: NCT04655924

J Clin Psychiatry 2025;86(3):24m15634

Author affiliations are listed at the end of this article.

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