ReviewFrontiers in digital health2024
Prompt engineering for digital mental health: a short review.
Review in Frontiers in digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Prompt engineering a large language model with evidence-based persuasive features to improve confidence in mental health professionals: a pilot randomized experiment.Frontiers in public health · 2026Trial
- Article
- The Goldilocks Zone: Finding the right balance of user and institutional risk for suicide-related generative AI queries.PLOS digital health · 2025Article
- From Llama to language: prompt-engineering allows general-purpose artificial intelligence to rate narratives like expert psychologists.Frontiers in artificial intelligence · 2025Article
- Adolescent mental health in rural settings: the role of artificial intelligence and community engagement.Frontiers in public health · 2025Review
- Self-help psychological intervention for young individuals during the post-COVID-19 era: development of a PST chatbot using GPT-4.Frontiers in digital health · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prompt engineering, the process of arranging input or prompts given to a large language model to guide it in producing desired outputs, is an emerging field of research that shapes how these models understand tasks, process information, and generate responses in a wide range of natural language processing (NLP) applications. Digital mental health, on the other hand, is becoming increasingly important for several reasons including early detection and intervention, and to mitigate limited availability of highly skilled medical staff for clinical diagnosis. This short review outlines the latest advances in prompt engineering in the field of NLP for digital mental health. To our knowledge, this review is the first attempt to discuss the latest prompt engineering types, methods, and tasks that are used in digital mental health applications. We discuss three types of digital mental health tasks: classification, generation, and question answering. To conclude, we discuss the challenges, limitations, ethical considerations, and future directions in prompt engineering for digital mental health. We believe that this short review contributes a useful point of departure for future research in prompt engineering for digital mental health.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.