Randomized Trial of AI-Guided Antidepressant Selection Improving Remission and Tolerability in Primary-Care Depression

Fatima Fatima; Fareeha F Khan; Anina Qureshi; Bushra Waheed; Eisha Maryam; Shaikh Khalid Muhammad

doi:10.61919/6ze48h29

Authors

Fatima Fatima Senior Registrar, Department of Psychiatry, Jinnah Medical College and Jinnah Teaching Hospital, Peshawar, Pakistan Author
Fareeha F Khan Senior Medical Officer, Intensive Care Unit, The Kidney Centre Hospital, Karachi, Pakistan Author
Anina Qureshi Assistant Professor, Margalla College of Pharmacy, Margalla Institute of Health Sciences; Department of Pharmacy, COMSATS University Islamabad, Abbottabad Campus; Department of Pharmacy, University of Sargodha, Rawalpindi, Pakistan Author
Bushra Waheed Lecturer, Department of Pharmacy, University of Swabi, Swabi, Pakistan Author
Eisha Maryam MPhil Student, Baghdad-ul-Jadeed Campus, The Islamia University of Bahawalpur, Bahawalpur, Pakistan Author
Shaikh Khalid Muhammad Professor of Medicine, Chandka Medical College Teaching Hospital, Shaheed Mohtarma Benazir Bhutto Medical University, Larkana, Pakistan Author

DOI:

https://doi.org/10.61919/6ze48h29

Keywords:

Algorithms; Antidepressive agents; Artificial intelligence; Clinical decision support; Depression; Patient Health Questionnaire-9; Primary health care; Randomised controlled trial; Treatment outcome.

Abstract

Background: Depressive disorders represent a leading cause of disability in primary-care settings globally, yet conventional antidepressant prescribing relies predominantly on trial-and-error approaches that delay remission and increase adverse-effect exposure. Artificial intelligence (AI)-based decision-support systems offer a mechanism for personalising medication selection by integrating patient-specific clinical data, but randomised evidence from real-world primary-care settings, particularly in low- and middle-income countries, remains absent. Objective: To compare AI-guided antidepressant selection with usual clinician-directed care on remission rates, adverse-effect burden, and time-to-clinical response among adults with depressive disorders in primary care. Methods: A prospective, parallel-group randomised controlled trial was conducted in primary-care clinics across South Punjab, Pakistan. Eighty-four adults (aged 18–65 years) with PHQ-9 scores ≥ 10 initiating or switching antidepressant therapy were randomised 1:1 to AI-guided medication selection (n = 42) or usual care (n = 42). The AI tool integrated baseline symptom profiles, comorbidity data, and prior treatment history to generate individualised first-line antidepressant recommendations. Depressive severity was assessed using the PHQ-9 at baseline and at weeks 2, 4, 6, and 8; remission was defined as PHQ-9 ≤ 4; clinical response as ≥ 50% PHQ-9 reduction from baseline; and adverse effects were measured using the FIBSER scale. Outcomes were analysed using independent-samples t-tests, chi-square tests, repeated-measures ANOVA, and effect sizes reported as Cohen's d. Results: At week eight, mean PHQ-9 score was significantly lower in the AI-guided group (6.16 ± 2.4 vs 8.47 ± 2.9; mean difference 2.31, 95% CI: 1.23–3.39; p < 0.001; d = 0.88). Remission was achieved by 61.9% of AI-guided participants versus 38.1% in usual care (RR = 1.63, 95% CI: 1.04–2.54; p = 0.029). Mean time to clinical response was 3.14 ± 0.9 versus 4.86 ± 1.1 weeks (difference 1.72 weeks, 95% CI: 1.29–2.15; p < 0.001; d = 1.73). Adverse-effect burden was significantly lower in the AI-guided arm (FIBSER: 3.16 ± 1.2 vs 4.80 ± 1.4; p < 0.001; d = 1.25). No serious adverse events were recorded in either group. Conclusion: AI-guided antidepressant selection improved remission rates reduced adverse-effect burden, and accelerated clinical response compared with usual care, with consistently large effect sizes across all outcome domains. These findings support integration of AI-based decision-support tools to enhance personalised depression treatment in resource-limited primary-care settings.

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