SYSTEMATIC REVIEW article
Front. Artif. Intell.
Sec. Medicine and Public Health
This article is part of the Research TopicAI with Insight: Explainable Approaches to Mental Health Screening and Diagnostic Tools in HealthcareView all 10 articles
Multimodal Observable Cues in Mood, Anxiety, and Borderline Personality Disorders: A Review of Reviews to Inform Explainable AI in Mental Health
Provisionally accepted
- 1Univerza v Mariboru Fakulteta elektrotehniko racunalnistvo informatiko, Maribor, Slovenia
- 2Department of Psychology, Faculty of Arts, University of Maribor, Maribor, Slovenia
- 3Univerzitetni Klinicni Center Maribor, Maribor, Slovenia
- 41Laboratory for Digital Signal Processing, Univerza v Mariboru Fakulteta elektrotehniko racunalnistvo informatiko, Maribor, Slovenia
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Mental health disorders, such as depression, anxiety, and borderline personality disorder (BPD), are common, often begin early, and can cause profound impairment. Traditional assessments rely heavily on subjective reports and clinical observation, which can be inconsistent and biased. Recent advances in AI offer a promising complement by analyzing objective, observable cues from speech, language, facial expressions, physiological signals, and digital behavior. Explainable AI ensures these patterns remain interpretable and clinically meaningful. A synthesis of 24 recent systematic and scoping reviews shows that depression is linked to self-focused negative language, slowed and monotonous speech, reduced facial expressivity, disrupted sleep and activity, and altered phone or online behavior. Anxiety disorders present with negative language bias, monotone speech with pauses, physiological hyperarousal, and avoidance-related behaviors. BPD exhibits more complex patterns, including impersonal or externally focused language, speech dysregulation, paradoxical facial expressions, autonomic dysregulation, and socially ambivalent behaviors. Some cues, like reduced heart rate variability and flattened speech, appear across conditions, suggesting shared transdiagnostic mechanisms, while BPD's interpersonal and emotional ambivalence stands out. These findings highlight the potential of observable, digitally measurable cues to complement traditional assessments, enabling earlier detection, ongoing monitoring, and more personalized interventions in psychiatry.
Keywords: Observable Cues, Mood Disorders, Anxiety Disorders, Borderline PersonalityDisorder, review, Explainable AI
Received: 31 Aug 2025; Accepted: 24 Nov 2025.
Copyright: © 2025 Močnik, Rehberger, Smogavc, Mlakar, Smrke and Močnik. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sara Močnik
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