Reading the unspoken: a systematic review of multimodal artificial intelligence for detecting psychological and affective states from nonverbal cues

  • Ifdil Ifdil 
    Univrsitas Negeri Padang, Padang
    Indonesia
  • Nur Adila Wafiqoh Zulvi
    Univrsitas Negeri Padang, Padang
    Indonesia
  • M. Fahli Zatrahadi
    Universitas Islam Negeri Sultan Syarif Kasim
    Indonesia

Abstract

The psychological assessment is limited to only relying on the patients' self-reports, as they may not even understand or have the desire or capacity to express the distress they show with their body language such as facial movements, voice, gaze, posture, and physiology. This systematic literature review brings together the findings of multimodal artificial intelligence (AI), mainly the use of such a system in detecting psychological and affective states from nonverbal cues and examines whether it is ready for mental-health and counseling applications. Using PRISMA 2020, we identified the records from a Scopus search, then screened them by the predefined eligibility criteria and evaluated them with the tool for Mixed-Method Appraisal. The original list of 669 articles was reduced first by removing the duplicates to 665 screened articles, and among them, we selected 20 articles released in 2024, 2025 that fulfilled the criteria. The themes identified in the five modality Channels facial & micro expression, speech & acoustic, text & linguistic, physiologic, and body movement together with their amalgamation were compared against the target states which were mainly depression, general emotion, and concealed affect. Multimodal approach generally yielded better results than unimodal methods and recognized concealed distress and suicidality, but the research is limited in scope, data sets being very small, external validation is missing and ethical precautions are not even considered yet.

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