Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023)
Article
Article Title | Artificial Intelligence-Based Suicide Prevention and Prediction: A Systematic Review (2019-2023) |
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ERA Journal ID | 20983 |
Article Category | Article |
Authors | Atmakuru, Anirudh, Shahini, Alen, Chakraborty, Subrata, Seoni, Silvia, Salvi, Massimo, Hafeez-Baig, Abdul, Rashid, Sadaf, Tan, Ru San, Barua, Prabal Datta, Molinari, Filippo and Acharya, U Rajendra |
Journal Title | Information Fusion |
Journal Citation | 114 |
Number of Pages | 29 |
Year | 2025 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 1566-2535 |
1872-6305 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.inffus.2024.102673 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1566253524004512 |
Abstract | Suicide is a major global public health concern, and the application of artificial intelligence (AI) methods, such as natural language processing (NLP), machine learning (ML), and deep learning (DL), has shown promise in advancing suicide prediction and prevention efforts. Recent advancements in AI – particularly NLP and DL have opened up new avenues of research in suicide prediction and prevention. While several papers have reviewed specific detection techniques like NLP or DL, there has been no recent study that acts as a one-stop-shop, providing a comprehensive overview of all AI-based studies in this field. In this work, we conduct a systematic literature review to identify relevant studies published between 2019 and 2023, resulting in the inclusion of 156 studies. We provide a comprehensive overview of the current state of research conducted on AI-driven suicide prevention and prediction, focusing on different data types and AI techniques employed. We discuss the benefits and challenges of these approaches and propose future research directions to improve the practical application of AI in suicide research. AI is highly capable of improving the accuracy and efficiency of risk assessment, enabling personalized interventions, and enhancing our understanding of risk and protective factors. Multidisciplinary approaches combining diverse data sources and AI methods can help identify individuals at risk by analyzing social media content, patient histories, and data from mobile devices, enabling timely intervention. However, challenges related to data privacy, algorithmic bias, model interpretability, and real-world implementation must be addressed to realize the full potential of these technologies. Future research should focus on integrating prediction and prevention strategies, harnessing multimodal data, and expanding the scope to include diverse populations. Collaboration across disciplines and stakeholders is essential to ensure that AI-driven suicide prevention and prediction efforts are ethical, culturally sensitive, and person-centered. |
Keywords | Artificial intelligence ; Natural language processing; Mental health; Machine learning; Suicide prevention; Suicide prediction |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 420313. Mental health services |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Massachusetts, United States |
PolitoBIOMed Lab, Italy | |
University of New England | |
University of Technology Sydney | |
Griffith University | |
School of Business | |
Sunshine Coast University Hospital, Australia | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
School of Mathematics, Physics and Computing |
https://research.usq.edu.au/item/zqqv6/artificial-intelligence-based-suicide-prevention-and-prediction-a-systematic-review-2019-2023
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