The Application of Large Language Models in Mental Healthcare
A special issue of Healthcare (ISSN 2227-9032). This special issue belongs to the section "Artificial Intelligence in Healthcare".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 1014
Editor
Special Issue Information
Dear Colleagues,
Large language models (LLMs) are rapidly entering mental healthcare—supporting triage, risk assessment, clinical documentation, therapy co-pilots, and patient-facing guidance. Yet key questions remain about their safety, reliability, equity, governance, and measurable clinical value.
In this Special Issue, we welcome original research and reviews on (i) safety science for LLMs in mental health (hazard taxonomies, red-teaming, alignment, guardrails, incident reporting); (ii) evaluation beyond accuracy (human-in-the-loop studies, clinician and service-user outcomes, cost-effectiveness, and workflow impact); (iii) methods for privacy-preserving and fair LLMs (de-identification, federated/edge models, bias/harms audits); (iv) applications across the care pathway (screening, symptom/functioning extraction, timeline building, decision support, crisis response); and (v) translation and regulation (governance frameworks, documentation standards, post-deployment monitoring).
We particularly welcome studies with clinical collaborators, representative patient data, and the transparent release of code, prompts, and evaluation protocols. By bringing together technical, clinical, and ethical perspectives, the aim of this Issue is to shape practical, safe, and equitable LLM adoption in mental healthcare.
Thank you for considering this invitation. We look forward to your contributions as we work together to advance the future of mental healthcare.
Dr. Tao Wang
Guest Editor
Manuscript Submission Information
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Keywords
- large language model (LLM)
- LLM safety
- clinical evaluation
- mental health informatics
- machine learning
- deep learning
- AI in mental healthcare
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