Currently submitted to: Interactive Journal of Medical Research
Date Submitted: Oct 31, 2025
Open Peer Review Period: Nov 10, 2025 - Jan 5, 2026
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Survey on Artificial Intelligence and Machine Learning Integration in Mental Health Practice: Narrative Review
ABSTRACT
Background:
Mental health disorders affect approximately one in five adults in the United States, yet nearly half of those who could benefit from treatment cannot access care due to provider shortages, cost, and stigma. Recent advances in machine learning and natural language processing have prompted interest in deploying artificial intelligence systems to augment clinical practice, automate administrative tasks, and expand treatment access.
Objective:
The objective of this review was to examine the impact of artificial intelligence on mental health professionals by analyzing current applications, benefits, and risks across three major domains: chatbot-based therapeutic tools, clinical documentation and automation, and diagnostic and clinical decision support systems (CDSS).
Methods:
A narrative literature review was conducted, analyzing peer-reviewed publications, professional organization position statements, and implementation studies focused on artificial intelligence applications in mental health practice. The review encompassed chatbot-based interventions, clinical documentation automation systems, and diagnostic support tools, with a focus on their efficacy, limitations, and implications for clinical practice.
Results:
The review found that AI-based interventions demonstrate small to moderate effectiveness in reducing depression and anxiety symptoms but show significant limitations including inappropriate crisis responses and reduced effectiveness compared to human therapists. AI documentation tools can reduce administrative burden but are vulnerable to hallucinations that insert fabricated information into clinical records. AI diagnostic support systems show preliminary promise but require further validation.
Conclusions:
While artificial intelligence applications demonstrate promise in reducing administrative burden and providing supplementary mental health support, substantial limitations persist in clinical effectiveness, crisis response capabilities, and reliability. Current evidence indicates artificial intelligence functions best as an augmentation tool requiring continuous clinical oversight rather than as standalone interventions or direct patient care. Future research directions are suggested to address these challenges, enhancing the reliability and applicability of artificial intelligence systems in mental health clinical practice.
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