Student Ph.D (Preprint)
Year: 2025
Type: preprint
Study design: Systematic Review
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. CLINICALTRIAL Not applicable. This manuscript is a systematic review and does not report results of a clinical trial.
Author: Scholas Mbonihankuye
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
OpenAlex ID: W4415032751
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