Large language models (LLMs) are rapidly reshaping digital mental health, yet how these systems are designed, used, and evaluated remains poorly characterized. We conducted an HCI-centered scoping review of 84 studies examining mental health tasks, stakeholders, interaction paradigms, foundation models, evaluation methods, and outcome measurement. Counseling and support and self-help and well-being accounted for 73.8% of studies, while clinician support and crisis and risk support remained uncommon.
Mixed-methods user studies predominated (42.9%), with only three randomized controlled trials and eight studies classified as longitudinal or field evaluations. Technical and response performance was assessed in 81.0% of studies, compared with 29.8% assessing mental health symptoms and clinical outcomes and 7.1% including longitudinal follow-up. Outcome profiles differed across intended tasks.
Building on these findings and established evaluation guidance, we propose a task-oriented framework aligning evaluation domains, measurement approaches, and assessment timing with intended use. Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, MN, USA Min Zeng, Zaifu Zhan, Kai Yu, Shuang Zhou, Yu Hou, Mingquan Lin & Rui Zhang Independent Researcher, Quanzhou, Fujian, China Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE Rui Zhang is an Associate Editor of npj Digital Medicine. He was not involved in the journal’s review of, or decisions related to, this manuscript.
The remaining authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.
You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Zeng, M., Zhan, Z., Yu, K. et al. Large language models for digital mental health: an HCI-centered scoping review. npj Digit.
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