Guardians of Trust: Risks and Opportunities for LLM s in Mental Health (2025)

Abstract: The integration of large language models (LLMs) into mental health applications offers promising opportunities for positive social impact. However, it also presents critical risks. While previous studies have often addressed these challenges and risks individually, a broader and multi-dimensional approach is still lacking. In this paper, we introduce a taxonomy of the main challenges related to the use of LLMs for mental health and propose a structured, comprehensive research agenda to mitigate them. We emphasize the need for explainable, emotionally aware, culturally sensitive, and clinically aligned systems, supported by continuous monitoring and human oversight. By placing our work within the broader context of natural language processing (NLP) for positive impact, this research contributes to ongoing efforts to ensure that technological advances in NLP responsibly serve vulnerable populations, fostering a future where mental health solutions improve rather than endanger well-being.

## introduction mental health is essential for a healthy life. however, mental health disorders are a growing global crisis. according to the world health organization mental health report 1 , it was estimated in 2019 that 970 million people worldwide suffered from a mental health disorder, which corresponds to a prevalence of 13 %. despite the increasing need for mental health support, access remains limited. over 75 % of people in low-income countries lack adequate services, and even in high-income ones like the united states, barriers such as cost, a lack of professionals, and social stigma still remain @xcite . in this context, large language models (llms) offer a new way to help reduce the existing gaps, 1 https://iris.who.int/bitstream/handle/10665/ 356119/9789240049338-eng.pdf data privacy training data user data privacy extraction outdated information adversarial attacks training-time inference-time bias and discrimination response inconsistency opacity lack of veracity low emotional intelligence persuasion & manipulation over-reliance poor crisis management data models users context lack of standardization not by replacing traditional professional support, but by providing an additional option. even though there are chatbots created specifically for mental health, it is now very common for people to use general-purpose llms as informal advisors for all sorts of questions, including mental health ones. however, using these technologies also raises ethical and safety questions that need to be carefully considered. building on previous taxonomies in mental health, such as those proposed by @xcite and @xcite , we note that these studies address several important risks and challenges, but their scope remains rather limited. in contrast, our approach introduces a multi-dimensional taxonomy that considers the full spectrum of risks. this taxonomy is both comprehensive and wellstructured, supported by a visual schema (figure 1 ) and a clearly organized framework. specifically, it consists of four dimensions: (1) data-related risks; (2) model-level concerns; (3) user-facing risks; and (4) contextual elements. building on this taxonomy, we propose a forward-looking research agenda to guide the safe use of llms for mental health. we emphasize that llms should augment, not replace, clinical judgment, and that these systems must be designed for continuous human oversight. together, these elements guide the safe and responsible deployment of llms in mental health applications, setting our work apart from existing surveys. ## risk taxonomy this section presents a taxonomy of risks that is both application-agnostic and transversal, organized according to the life cycle of llm development and deployment: data, model, user, and context. ## research agenda given the previously described risks, we propose ideas and possible directions for future research that could improve the safety and effectiveness of llms used in mental health. these suggestions are based on what authors have proposed in the areas studied, and are introduced as promising avenues to explore rather than as solutions to be directly implemented. ## conclusion as the integration of llms into mental health applications continues to expand, it is important to detect and handle the different risks that may affect their effectiveness, reliability, and ethical implications. in this article, we have presented a taxonomy of risks and a structured agenda of key research directions that are needed to address these challenges. llms offer great potential to improve mental health care, although their implementation must be carefully designed, regulated, and evaluated. only the implementation of fairer, more reliable, safer, and ethically aligned models will make it possible to achieve a useful and beneficial integration of llms in the field of mental health.