Editorial: Empowering suicide prevention efforts with generative AI technology

Suicide Suicide claims approximately 746,000 lives each year, ranking among the leading causes of premature mortality and psychological distress worldwide (Weaver et al., 2025).Precision risk assessment is especially challenging for high-vulnerability groups, such as military veterans, middle-aged men, and LGBTQ+ individuals. Additional challenges are posed by multiple aspects of stigma (internalized, anticipated, and public), which impedes help-seeking behaviors (Carpiniello Nobile et al., 2024).In recent years, the rapid advancement of Artificial Intelligence (AI), particularly Generative AI and the large language models (LLMs) that power it, has opened new avenues for suicide risk assessment, prevention, and intervention. Emerging evidence suggests that these technologies can contribute to more personalized and scalable screening tools, enhance the training of mental health professionals, reduce stigma, and support early detection in clinical and digital environments (Levkovich Shinan-Altman et al., 2024).Researchers have also explored how cultural context can influence AI's sensitivity to suicide risks (Levkovich et al., 2024) and how clinical variables such as history of depression, previous suicide attempts, or access to weapons can be integrated into AI models to improve prediction accuracy (Shinan-Altman et al., 2024).Generative AI technologies can be ethically and effectively harnessed to improve suicide prevention. The included articles offer diverse perspectives on technological applications, clinical insights, and ethical considerations with the aim of promoting evidence-based innovation in one of the most urgent areas of mental health.The articles included in this special issue demonstrate the multidisciplinary potential of Generative Artificial Intelligence (GenAI) and large language models (LLMs) to advance suicide prevention. The researchers apply these technologies across diverse contexts, including risk assessment, professional training, public health monitoring, and qualitative analysis, leveraging machine learning to identify previously overlooked risk factors, improve diagnostic accuracy, and support complex clinical decision-making.The study by Lissak et al. (2024) highlights boredom, particularly disengaged boredom, as a significant risk factor for suicide. This conclusion was reached through a hybrid approach that combined large-scale natural language processing with validated psychological measures. Lauderdale et al. (2025) While the model demonstrated strong capacity for identifying central themes, the authors also reported concerns related to conceptual inaccuracies and overgeneralizations.Together, these studies illustrate both the promise as well as the complexity of integrating GenAI into suicide prevention efforts. They emphasize the need for ongoing refinement of AI models, close collaboration with clinical professionals, and the application of ethical frameworks that ensure responsible, context-sensitive, and human-centered implementation.Generative AI holds transformative potential for suicide prevention, but progress demands a multidisciplinary, ethically grounded approach. As highlighted in several articles in this issue, there is an urgent need to enhance model transparency, interpretability, and contextual sensitivity in both clinical and cultural settings (Shinan-Altman, Elyoseph, and Levkovich, 2024;Levkovich, Shinan-Altman, and Elyoseph, 2024). These technologies should be viewed not as replacements for clinical expertise, but as supportive tools that assist in making ethically grounded and context-aware decisions.There are clear opportunities in developing personalized interventions, emotionally rich training simulations, and novel methods for detecting hidden suicide risk factors. For instance, boredom was identified as a central predictor of suicidality (Lissak et al., 2024), while other studies demonstrated GenAI's capacity to assess risk in high-vulnerability populations such as veterans (Lauderdale et al., 2025) and leukemia patients (Zheng et al., 2025). In parallel, Generative AI systems may help reduce stigma and encourage helpseeking behavior in marginalized communities (Levkovich and Omar, 2024).Nevertheless, important challenges remain. These include algorithmic bias, digital inequities, and cultural variability in the expression and recognition of distress (Nobile et al., 2024;Omar et al., 2025;Schnepper et al., 2025). AI-based tools must be designed with careful attention to gender, cultural diversity, and clinical nuance to ensure fairness and relevance across populations.Meaningful progress in the field will require collaboration across disciplines, involving clinicians, ethicists, computer scientists, and policymakers, to establish ethical and regulatory frameworks that protect human dignity (Grodniewicz et al., 2024). As illustrated in the work of Balt et al. (2025), human feedback and iterative review can strengthen the validity of AI outputs and foster user trust.As technological capabilities continue to expand, the application of Generative AI in suicide prevention must be guided by principles of responsibility, inclusivity, and a sustained commitment to the wellbeing of vulnerable individuals. Its success will depend on thoughtful implementation, ongoing professional oversight, and continuous ethical engagement.The contributions to this Research Topic illustrate the transformative potential of Generative AI in suicide prevention. Across quantitative, qualitative, and theoretical frameworks, the included studies demonstrate how LLMs can support more nuanced risk detection, automate complex coding processes, and uncover novel psychological risks. Simultaneously, the limitations of these technologies, ranging from ethical concerns to contextual misinterpretation, reinforce the need for responsible implementation. Collectively, these studies offer a foundation for AI-assisted suicide-prevention tools -tools that must always complement, not replace, expert human judgement, cultural sensitivity, and empirical rigor.