Back to top Open menu arrow
Purchase PDF

Save Save

Cite Cite

Reprints
Sections
METHODS RESULTS DISCUSSION CONCLUSIONS Article Information Clinical Points
The Journal of Clinical Psychiatry The Journal of Clinical Psychiatry

The Journal of
Clinical Psychiatry
Access icon

Original Research Early Career Psychiatrists August 27, 2025

Artificial Intelligence in Depression–Medication Enhancement (AID-ME): A Cluster Randomized Trial of a Deep-Learning-Enabled Clinical Decision Support System for Personalized Depression Treatment Selection and Management

; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ;

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

Abstract

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.

Members Only Content

This full article is available exclusively to Professional tier members. Subscribe now to unlock the HTML version and gain unlimited access to our entire library plus all PDFs. If you’re already a subscriber, please log in below to continue reading.

Subscribe Now

Already a member? Log in

  1. Greenberg PE, Fournier A-A, Sisitsky T, et al. The economic burden of adults with major depressive disorder in the United States (2005 and 2010). J Clin Psychiatry. 2015;76(2):155–162. PubMed CrossRef
  2. World Health Organization. Depression and Other Common Mental Disorders: Global Health Estimates. World Health Organization; 2017.
  3. Rush AJ, Trivedi MH, Wisniewski SR, et al. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry. 2006;163(11):1905–1917. PubMed CrossRef
  4. Kraus C, Kadriu B, Lanzenberger R, et al. Prognosis and improved outcomes in major depression: a review. Transl Psychiatry. 2019;9(1):127. PubMed CrossRef
  5. Benrimoh D, Fratila R, Israel S, et al. Aifred Health, a deep learning powered clinical decision support system for mental health. In: The NIPS’17 Competition: Building Intelligent Systems. Springer International Publishing;2018:251–287. CrossRef
  6. Mehltretter J, Rollins C, Benrimoh D, et al. Analysis of features selected by a deep learning model for differential treatment selection in depression. Front Artif Intell. 2019;2:31. PubMed CrossRef
  7. Squarcina L, Villa FM, Nobile M, et al. Deep learning for the prediction of treatment response in depression. J Affect Disord. 2021;281:618–622. PubMed CrossRef
  8. Poon AIF, Sung JJY. Opening the black box of AI-Medicine. J Gastroenterol Hepatol. 2021;36(3):581–584. PubMed CrossRef
  9. Maslej MM, Kloiber S, Ghassemi M, et al. Out with AI, in with the psychiatrist: a preference for human-derived clinical decision support in depression care. Transl Psychiatry. 2023;13(1):210. PubMed CrossRef
  10. Celi LA, Cellini J, Charpignon ML, et al. Sources of bias in artificial intelligence that perpetuate healthcare disparities-A global review. PLOS Digit Health. 2022;1(3):e0000022. PubMed CrossRef
  11. Schneider F, Kratz S, Bermejo I, et al. Insufficient depression treatment in outpatient settings. Ger Med Sci. 2004;2:Doc01. PubMed
  12. Lisinski A, Hieronymus F, Eriksson E, et al. Low SSRI dosing in clinical practice-a register-based longitudinal study. Acta Psychiatr Scand. 2021;143(5):434–443. PubMed CrossRef
  13. von Knorring J, Baryshnikov I, Jylhä P, et al. Prospective study of antidepressant treatment of psychiatric patients with depressive disorders: treatment adequacy and outcomes. BMC Psychiatry. 2023;23(1):888. PubMed CrossRef
  14. Golden G, Popescu C, Israel S, et al. Applying artificial intelligence to clinical decision support in mental health: what have we learned? Health Pol Technology. 2022;13(2):100844.
  15. Kennedy SH, Lam RW, McIntyre RS, et al. Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder: Section 3. Pharmacological Treatments. Can J Psychiatry. 2016;61(9):540–560. PubMed CrossRef
  16. Benrimoh D, Tanguay-Sela M, Perlman K, et al. Using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence-powered clinical decision support system for depression treatment on the physician-patient interaction. BJPsych Open. 2021;7(1):e22. PubMed CrossRef
  17. Tanguay-Sela M, Benrimoh D, Popescu C, et al. Evaluating the perceived utility of an artificial intelligence-powered clinical decision support system for depression treatment using a simulation center. Psychiatry Res. 2022;308:114336. PubMed CrossRef
  18. Popescu C, Golden G, Benrimoh D, et al. Evaluating the clinical feasibility of an artificial intelligence-powered, web-based clinical decision support system for the treatment of depression in adults: longitudinal feasibility study. JMIR Form Res. 2021;5(10):e31862. PubMed CrossRef
  19. Qassim S, Golden G, Slowey D, et al. A mixed-methods feasibility study of a novel AI-enabled, web-based, clinical decision support system for the treatment of major depression in adults. J Affect Disord Rep. 2023;14:100677. CrossRef
  20. Mehltretter J, Fratila R, Benrimoh D, et al. Differential treatment Benet prediction for treatment selection in depression: a deep learning analysis of STAR*D and CO-MED data. Comput Psychiatr. 2020;4:61. CrossRef
  21. Kleinerman A, Rosenfeld A, Benrimoh D, et al. Treatment selection using prototyping in latent-space with application to depression treatment. PLoS One. 2021;16(11):e0258400. PubMed CrossRef
  22. Benrimoh D, Kleinerman A, Furukawa TA, et al. Towards outcome-driven patient subgroups: a machine learning analysis across six depression treatment studies. Am J Geriatr Psychiatry. 2024;32(3):280–292. PubMed CrossRef
  23. Liu X, Rivera SC, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370:m3164. PubMed CrossRef
  24. Cook AJ, Delong E, Murray DM, et al. Statistical lessons learned for designing cluster randomized pragmatic clinical trials from the NIH Health Care Systems Collaboratory Biostatistics and Design Core. Clin Trials. 2016;13(5):504–512. PubMed CrossRef
  25. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). American Psychiatric Publishing; 2013.
  26. Sheehan DV, Lecrubier Y, Sheehan KH, et al. The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J Clin Psychiatry. 1998;59(suppl 20):22–57. PubMed
  27. Asberg M, Montgomery SA, Perris C, et al. A comprehensive psychopathological rating scale. Acta Psychiatr Scand Suppl. 1978(271):5–27. PubMed CrossRef
  28. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. PubMed CrossRef
  29. Benrimoh D, Armstrong C, Mehltretter J, et al. Development and validation of a deep-learning model for differential treatment benefit prediction for adults with major depressive disorder deployed in the Artificial Intelligence in Depression Medication Enhancement (AIDME) study. arXiv [q-bio.NC]; 2024. https://doi.org/10.48550/arXiv.2406.04993
  30. Perlman K, Mehltretter J, Benrimoh D, et al. Development of a differential treatment selection model for depression on consolidated and transformed clinical trial datasets. Transl Psychiatry. 2024;14(1):263. PubMed CrossRef
  31. Fleischhacker WW, Czobor P, Hummer M, et al. Placebo or active control trials of antipsychotic drugs? Arch Gen Psychiatry. 2003;60(5):458–464. PubMed CrossRef
  32. Center for Drug Evaluation and Research. E10 choice of control group and related issues in clinical trials. U.S. Food and Drug Administration; 2020. Accessed February 8, 2024. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e10-choice-control-group-and-related-issues-clinical-trials.
  33. Byerly MJ, Nakonezny PA, Rush AJ. The Brief Adherence Rating Scale (BARS) validated against electronic monitoring in assessing the antipsychotic medication adherence of outpatients with schizophrenia and schizoaffective disorder. Schizophr Res. 2008;100(1–3):60–69. PubMed CrossRef
  34. McIntyre RS, Konarski JZ, Mancini DA, et al. Measuring the severity of depression and remission in primary care: validation of the HAMD-7 scale. CMAJ. 2005;173(11):1327–1334. PubMed CrossRef
  35. Kaneriya SH, Robbins-Welty GA, Smagula SF, et al. Predictors and moderators of remission with aripiprazole augmentation in treatment-resistant late-life depression: an analysis of the IRL-GRey randomized clinical trial. JAMA Psychiatry. 2016;73(4):329–336. PubMed CrossRef
  36. Page SJ, Persch AC. Recruitment, retention, and blinding in clinical trials. Am J Occup Ther. 2013;67(2):154–161. PubMed CrossRef
  37. Williams JBW, Kobak KA. Development and reliability of a structured interview guide for the Montgomery Asberg Depression Rating Scale (SIGMA). Br J Psychiatry. 2008;192(1):52–58. PubMed CrossRef
  38. Hengartner MP, Plöderl M. Estimates of the minimal important difference to evaluate the clinical significance of antidepressants in the acute treatment of moderate-to-severe depression. BMJ Evid Based Med. 2022;27(2):69–73. PubMed CrossRef
  39. Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. PubMed CrossRef
  40. Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–2410. PubMed CrossRef
  41. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–36. PubMed CrossRef
  42. Rush AJ, Trivedi M, Carmody TJ, et al. One-year clinical outcomes of depressed public sector outpatients: a benchmark for subsequent studies. Biol Psychiatry. 2004;56(1):46–53. PubMed CrossRef
  43. Greden JF, Parikh SV, Rothschild AJ, et al. Impact of pharmacogenomics on clinical outcomes in major depressive disorder in the GUIDED trial: a large, patient-and rater-blinded, randomized, controlled study. J Psychiatr Res. 2019;111:59–67. PubMed CrossRef

About the Authors

Buy PDF for $40

Please sign in or purchase this PDF for $40.

Save Save

Cite Cite

Vol 86 • 2025 • Number 2

Read the Current Issue

Original Research

Incidence and Risk Factors for Suicide Attempt During Pregnancy and Postpartum

Original Research

Valbenazine in Elderly Adults With Tardive Dyskinesia

Academic Highlights

Optimizing Treatment With Aripiprazole Monohydrate: Pharmacokinetic Advantages of Long-Acting Injectable Formulations, A Consensus Panel Report

Original Research

Older Adults Visiting Emergency Departments for Mental Health Issues:A CHIRPP Database Study

Consensus Statement

Developing a Treatment-Resistant Depression Consultation Program, Part I: Practical and Logistical Considerations

Brief Report

Psychosocial Correlates of Optimism among US Military Veterans
View current Issue

Watch the New PsychCase360

Explore two real-world schizophrenia cases

Subscribe to read
more articles

Subscribe