Multimodal observable cues in mood, anxiety, and borderline personality disorders: a review of reviews to inform explainable AI in mental health.

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.