Mental healthcare faces a growing accessibility crisis as demand far exceeds the supply of qualified professionals, creating urgent pressure to identify scalable intervention tools. Large language models represent a potentially transformative technology that could democratize basic mental health screening and support, particularly for common conditions where early detection dramatically improves outcomes.
This comprehensive analysis of 29 studies reveals that AI language systems demonstrate particular strength in automated screening for depression and other high-prevalence mental health disorders. The research indicates a strategic pivot from general-purpose models toward specialized BERT-based architectures that achieve superior clinical accuracy in specific domains. These systems excel at processing large volumes of text-based mental health data and identifying linguistic patterns associated with psychological distress, offering potential for population-level screening initiatives.
However, the current evidence base exposes significant implementation barriers that temper initial enthusiasm. The reviewed studies predominantly focus on narrow screening applications rather than comprehensive diagnostic or therapeutic interventions, suggesting the technology remains in early developmental phases. Critical gaps include limited validation across diverse populations, insufficient longitudinal outcome data, and unclear integration pathways with existing clinical workflows. The ethical implications of deploying AI systems in vulnerable mental health contexts also demand careful consideration, particularly regarding privacy, algorithmic bias, and the risk of over-reliance on automated assessments. While these findings suggest meaningful potential for augmenting clinical capacity, the technology appears better suited for supportive roles rather than replacing human clinical judgment in mental healthcare delivery.