This paper is in the following e-collection/theme issue:

Scoping Review Protocols (252) Methods and New Tools in Mental Health Research (561) Clinical Information and Decision Making (1931) Artificial Intelligence (2157) Generative Language Models Including ChatGPT (722) Ethics, Privacy, and Legal Issues (622)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/62865, first published .
Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review

Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review

Exploring the Credibility of Large Language Models for Mental Health Support: Protocol for a Scoping Review

Authors of this article:

Dipak Gautam1 Author Orcid Image ;   Philipp Kellmeyer2, 3, 4 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Zhou J, Cheng Y, He S, Chen Y, Chen H. Large Language Models for Transforming Healthcare: A Perspective on DeepSeek‐R1. MedComm – Future Medicine 2025;4(2) View
  2. Wang S, Zhao F, Bu D, Lu Y, Gong M, Liu H, Yang Z, Zeng X, Yuan Z, Wan B, Sun J, Wu Y, Zhao L, Wan X, Huang W, Wang T, Xu M, Luo J, Liu J, Zheng J, Zhang W, Zhang K, Zhang H, Wang S, Chen R, Zhao Y. LINS: A general medical Q&A framework for enhancing the quality and credibility of LLM-generated responses. Nature Communications 2025;16(1) View

Books/Policy Documents

  1. Fu M, Chan W. Oxford Intersections: AI in Society. View