Beyond what people say: a systematic review of artificial intelligence-based detection of psychological non-disclosure and hidden mental states in counseling and mental health care

Jurnal Riset Tindakan Indonesia September 21, 2026 Volume 10, Number 4 3 authors

Abstract

Not revealing psychologically distressing thoughts, symptoms, and experiences, that is, psychological non-disclosure, contributes to the failure of detection and treatment in many cases of mental illness. Since diagnostic methods mostly depend on self-reporting, such states often remain unrecognized. This article is a literature review and it summarizes the research on how AI and computational methods can be applied to detect psychological non-disclosure and secret states. According to PRISMA 2020, literature was searched using three databases: Scopus, PubMed, and ScienceDirect; only eligible sources were selected and the Mixed Methods Appraisal Tool was used for evaluating. After excluding duplicates only 791 records remained with 20 of those (2020-2025) being included. Six methods are mentioned: language and text analysis; speech and acoustic biomarkers; facial micro-expressions and physiological signals; large language model conversational systems; predictive machine learning; and disclosure augmentation. The study shows that multimodal AI can identify hidden distress, self-injury, and suicidal states although the knowledge is still not fully developed, validation is not enough, and safeguards against misuse remain weak. A framework mapping the different modalities to possible targets was suggested as well the article highlights the need for more thorough validation, transparent reporting, and privacy-preserving use of AI.

Authors

  1. Ifdil Ifdil

    Univrsitas Negeri Padang, Padang

  2. Nur Adila Wafiqoh Zulvi

    Univrsitas Negeri Padang, Padang

  3. M. Fahli Zatrahadi

    Universitas Islam Negeri Sultan Syarif Kasim

Keywords

No keywords available

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