Natural Language Processing for Mental Health (Dagstuhl Seminar 25361)

Abstract

NLP has made remarkable progress in recent years, driven by breakthroughs in large language models (LLMs) and the availability of large-scale datasets such as social media posts, online forums, and patient records. These advances have made NLP models highly capable of extracting valuable insights from text data related to mental health. This development raises two natural questions: (1) How well, if at all, can NLP enable early detection, diagnosis, and intervention - not only for patients or support seekers but also for therapists or support providers? (2) Can NLP-driven solutions help bridge the gap between the escalating demand for mental health resources and the limited availability of mental health professionals, providing scalable and immediate support through chatbots, virtual therapists, and data-driven interventions? Both questions address the technical feasibility and the ethical concerns about using a developing technology in a sensitive application. This Dagstuhl Seminar brought together researchers across NLP, clinical science, human–computer interaction, and digital mental health to reflect on how NLP and AI can support mental health outcomes. Over the course of the week, we looked at key areas where NLP has the potential to transform mental health: understanding how mental states change and how therapeutic change occurs; exploring how NLP can help therapist training and feedback; identifying technological gaps and multilingual challenges in building reliable mental health models; and addressing pressing concerns around evaluation, validation, privacy, and ethics. Through vision talks, lightning sessions, and breakout groups, participants explored both the opportunities and limitations of deploying NLP for mental health, laying the groundwork for responsible, interdisciplinary research in this vital direction.