Mental-health AI review maps uses and gaps in clinical evidence
A scoping review describes how language models are being studied for screening and clinical support, alongside concerns about cultural sensitivity, patient history and misleading information.
Based on the published abstract. The full paper may contain additional methods, results and limitations.
The 30-second takeaway
The review maps research on language models used for mental-health screening and clinical support. It identifies practical and ethical limits, including difficulty incorporating a person's history and the risk of misleading information. The abstract does not report improvements in patient symptoms or establish that these systems are safe substitutes for mental-health care.
The abstract describes research themes without reporting patient outcome estimates or a shared clinical comparison. It cannot establish effectiveness, safety or how well any particular system performs for a specific population.
What the review found
The authors describe a focus on screening many records and supporting clinicians. Studies used large collections of social-media text alongside clinical knowledge, with attention shifting toward specialized models for particular mental-health tasks.
Where the boundaries remain
The review flags weak cultural sensitivity outside Western settings, difficulty tracking patient history over time and risks of clinically misleading responses. These concerns describe gaps to investigate, rather than quantified rates of harm or error.
The original publication
Exploring the application boundaries of LLMs in mental health: a systematic scoping review.
Yang J, Liu T, Luo YT et al.
Front Psychol · 2025
- PubMed ID
- 41836276
- Record checked
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