From internal accuracy to clinical readiness: a systematic review of artificial intelligence-based mental health studies in Southeast Asia.

Abstract

Artificial intelligence (AI) is increasingly applied to mental health research, but internal performance does not establish clinical or public health readiness. To synthesise this regional AI literature and assess validation, reporting transparency, reproducibility, implementation-readiness, and clinical readiness. Scopus, PubMed, and IEEE Xplore were searched from inception to March 26, 2026. Eligible records were English-language peer-reviewed articles or conference proceedings applying AI, machine learning, or deep learning to mental health or psychiatric outcomes. Ninety-nine studies were included. Evidence was concentrated in Indonesia, Malaysia, and Thailand, and more than half addressed depression-spectrum conditions. Studies mainly used questionnaires, clinical-tabular data, social media, or digital-trace data for classification, detection, or severity stratification. Validation was almost entirely internal; no study reported external or independent validation. Calibration, uncertainty, error analysis, fairness or bias assessment, code or data availability, and implementation context were rarely reported. No study met the criteria for high clinical readiness. The regional evidence base is expanding, but remains concentrated at the model-development and internal-validation stages. Future work should prioritise representative datasets, transparent reporting, privacy-preserving external validation across settings and languages, and evaluation linked to mental health services or public health workflows. The proposed clinical translation roadmap supports the development of reproducible, workflow-aligned, and clinically meaningful AI applications.