ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2024
Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data.
Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers, 3 of them syntheses that pooled it.
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Who cites it
71 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Computational methods for the identification of suicidal ideation: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review.JMIR mental health · 2025Pooled it
- Empathic Conversational Agent Platform Designs and Their Evaluation in the Context of Mental Health: Systematic Review.JMIR mental health · 2024Pooled it
- Interaction of artificial intelligence, mental disorders, and diverse data modalities: Potential treatment management based on the "method-disease-data" axis.Neural regeneration research · 2026Article
- Fine-tuning large language models for detecting negative self-referential language in smartphone text among youth with depression.Journal of psychopathology and clinical science · 2026Article
- Scalable, context-sensitive psychiatric assessment with large language models and brief diaries.Psychological medicine · 2026Article
- Article
- Towards automated joint detection in sleep studies: bridging clinical insight and artificial intelligence.Sleep · 2026Article
- Analyzing Generative AI and Machine Learning in Auto-Assessing Schizophrenia's Negative Symptoms.Schizophrenia bulletin · 2026Article
- Rethinking AI in youth mental health: promise, perils, and ethical integration.International journal of mental health systems · 2026Article
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- Current Landscape of Mental Health Conversational Agents From a Trauma-Informed Care Lens: Scoping Review.JMIR mental health · 2026Article
- Interpretable depressive symptoms screening via statistical reasoning-augmented large language models using wearable and environmental data.Scientific reports · 2026Article
- Predicting Momentary Suicidal Ideation From Smartphone Screenshots Using Vision-Language Models: Prospective Machine Learning Study.JMIR mental health · 2026Article
- Exploring Collaboration Breakdowns Between Provider Teams and Patients in Post-Surgery Care.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2026Article
- In their own words: case studies of adolescent smartphone language preceding suicide-related hospitalizations.NPP - digital psychiatry and neuroscience · 2026Article
- Review
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- Adaptive emotion-aware chatbot for mental health diagnosis using recurrent reinforcement learning and transformer models.Frontiers in artificial intelligence · 2026Article
- SPEAK-SAFE: secure processing of electronic audio for knowledge in suicide assessment from therapeutic exchanges.Frontiers in digital health · 2026Article
11 more citing papers are in PubMed but not listed here.
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9 authors.
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Abstract
Advances in large language models (LLMs) have empowered a variety of applications. However, there is still a significant gap in research when it comes to understanding and enhancing the capabilities of LLMs in the field of mental health. In this work, we present a comprehensive evaluation of multiple LLMs on various mental health prediction tasks via online text data, including Alpaca, Alpaca-LoRA, FLAN-T5, GPT-3.5, and GPT-4. We conduct a broad range of experiments, covering zero-shot prompting, few-shot prompting, and instruction fine-tuning. The results indicate a promising yet limited performance of LLMs with zero-shot and few-shot prompt designs for mental health tasks. More importantly, our experiments show that instruction finetuning can significantly boost the performance of LLMs for all tasks simultaneously. Our best-finetuned models, Mental-Alpaca and Mental-FLAN-T5, outperform the best prompt design of GPT-3.5 (25 and 15 times bigger) by 10.9% on balanced accuracy and the best of GPT-4 (250 and 150 times bigger) by 4.8%. They further perform on par with the state-of-the-art task-specific language model. We also conduct an exploratory case study on LLMs' capability on mental health reasoning tasks, illustrating the promising capability of certain models such as GPT-4. We summarize our findings into a set of action guidelines for potential methods to enhance LLMs' capability for mental health tasks. Meanwhile, we also emphasize the important limitations before achieving deployability in real-world mental health settings, such as known racial and gender bias. We highlight the important ethical risks accompanying this line of research.
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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.