Previously submitted to: JMIR Mental Health (no longer under consideration since Oct 10, 2025)
Date Submitted: Oct 10, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Student Ph.D
ABSTRACT
Background:
Traditional mental health interventions face challenges like delayed diagnosis and access gaps. While narrow AI offers task-specific solutions, it lacks the holistic capabilities needed for comprehensive care.
Objective:
This review explores the potential of Artificial General Intelligence (AGI)—systems with human-like cognitive abilities—to revolutionize early detection and prevention in mental health, identifying key applications and implementation challenges.
Methods:
A systematic literature review was conducted using the PRISMA framework across four major databases (IEEE Xplore, PubMed, ACM Digital Library, ScienceDirect) from 2020-2025. From 1,445 initial records, 50 studies were included and thematically analyzed.
Results:
AGI enables transformative applications like multimodal early detection, personalized interventions, and emotion-aware systems by integrating diverse data streams (e.g., speech, text, biometrics). Significant challenges include algorithmic bias, data privacy, limited empathy, and clinical integration hurdles.
Conclusions:
AGI can shift mental healthcare toward proactive, scalable prevention. Responsible deployment requires interdisciplinary collaboration to address ethical concerns, enhance fairness, ensure explainability, and establish robust regulatory frameworks. Clinical Trial: Not applicable. This manuscript is a systematic review and does not report results of a clinical trial.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.