ARTIFICIAL INTELLIGENCE IN THE SUICIDE PREVENTION - A SYSTEMATIC REVIEW

Olga Jakubik; Katarzyna Torbacka; Katarzyna Rozkosz; Natalia Wróbel; Patrycja Podlejska; Maja Torbacka; Zuzanna Wróbel; Aleksandra Sosin; Joanna Kaczor; Wojciech Bednarz

doi:10.31435/ijitss.4(48).2025.4447

Authors

Olga Jakubik Praski Hospital, Sp. z o.o., al. "Solidarności" 67, 03-401 Warszawa, Poland https://orcid.org/0009-0006-4378-7087
Katarzyna Torbacka 5 Military Clinical Hospital with Polyclinic SPZOZ, Wrocławska 1/3, 30-901 Kraków, Poland https://orcid.org/0009-0002-7208-3725
Katarzyna Rozkosz Independent Public Health Care Center of the Ministry of the Interior and Administration in Katowice named after sergeant Grzegorz Załoga, Wita Stwosza 39-41, 40-042 Katowice, Poland https://orcid.org/0009-0000-3610-1901
Natalia Wróbel 5 Military Clinical Hospital with Polyclinic SPZOZ, Wrocławska 1/3, 30-901 Kraków, Poland https://orcid.org/0009-0005-9236-9802
Patrycja Podlejska Independent Public Health Care Center of the Ministry of the Interior and Administration in Katowice named after sergeant Grzegorz Załoga, Wita Stwosza 39-41, 40-042 Katowice, Poland https://orcid.org/0009-0002-0074-9778
Maja Torbacka Jagiellonian University Collegium Medicum, Świętej Anny 12, 31-008 Kraków, Poland https://orcid.org/0009-0000-7139-2134
Zuzanna Wróbel The West Hospital in Grodzisk Mazowiecki, Daleka 11, 05-825 Grodzisk Mazowiecki, Poland https://orcid.org/0009-0005-7529-0641
Aleksandra Sosin Jagiellonian University Collegium Medicum, Świętej Anny 12, 31-008 Kraków, Poland https://orcid.org/0009-0008-0440-5431
Joanna Kaczor Independent Public Healthcare Institution in Myślenice, Szpitalna 2, 32-400 Myślenice, Poland https://orcid.org/0009-0002-1714-2630
Wojciech Bednarz Independent Public Healthcare Institution of the Ministry of the Interior and Administration in Kraków, Kronikarza Galla 25, 30-053 Kraków, Poland https://orcid.org/0009-0003-9522-6298

DOI:

https://doi.org/10.31435/ijitss.4(48).2025.4447

Keywords:

Suicide Prevention, Artificial Intelligence, AI, Machine Learning, ML, Mental Health, Digital Psychiatry

Abstract

Objectives: Suicide remains a pressing global public health issue, claiming over 700,000 lives annually. Despite decades of research, predictive models for suicide risk have seen limited progress, emphasizing the need for innovative approaches. Artificial intelligence (AI) and machine learning (ML) are increasingly recognized for their potential to enhance early detection, risk assessment, and intervention strategies. The objective of this review is to provide a critical evaluation of the role of AI in the detection and prevention of suicide, with particular emphasis on its current implementations, potential benefits, and inherent limitations.

Methods: A literature review was conducted for the period spanning 2020 to 2025. Relevant publications were identified through a systematic search of the PubMed database using the keywords: artificial, intelligence, suicide, and prevention. The search retrieved 271 articles, of which 23 met the predefined inclusion criteria and were incorporated into the final analysis.

Key findings: Recent developments highlight how AI technologies can support suicide prevention through social media surveillance, clinical decision-making tools, and real-time crisis response systems. However, these advancements are not without challenges. Significant concerns persist around data privacy, algorithmic bias, transparency, and the erosion of human-centered care.

Conclusions: While AI-driven tools offer substantial opportunities for suicide prevention, their integration into mental health care must be approached with caution. Ethical safeguards, clinical oversight, and continued research are essential to ensure these technologies complement—rather than compromise—traditional human care. A balanced, multidisciplinary approach is vital to realize the full potential of AI while maintaining patient trust, safety, and dignity.

References

World Health Organization. Suicide worldwide in 2019: global health estimates. Geneva: World Health Organization, 2021.

González-Ortega, I., Diaz-Marsa, M., López-Peña, P., Fernández-Sevillano, J., Andreo-Jover, J., Bobes, J., Bravo-Ortiz, M. F., Cebria, A. I., Crespo-Facorro, B., de la Torre-Luque, A., Elices, M., Fernández-Rodrigues, V., Garrido-Torres, N., Grande, I., Palao-Tarrero, Á., Pemau, A., Roberto, N., Ruiz-Veguilla, M., Seijo-Zazo, E., Alberich, S., … SURVIVE Group (2025). Clinical predictors and psychosocial risk factors of suicide attempt severity. Spanish journal of psychiatry and mental health, 18(3), 182–190. https://doi.org/10.1016/j.sjpmh.2023.07.002O'Connor RC, Nock MK. The psychology of suicidal behaviour. Lancet Psychiatry. 2014 Jun;1(1):73-85. doi: 10.1016/S2215-0366(14)70222-6.

Moutier, C., & Mortali, M. G. (2021). Suicide Warning Signs and What to Do. The Veterinary clinics of North America. Small animal practice, 51(5), 1053–1060. https://doi.org/10.1016/j.cvsm.2021.04.021Li X, Mu F, Liu D, Zhu J, Yue S, Liu M, Liu Y, Wang J. Predictors of suicidal ideation, suicide attempt and suicide death among people with major depressive disorder: A systematic review and meta-analysis of cohort studies. J Affect Disord. 2022 Apr 1;302:332-351. doi: 10.1016/j.jad.2022.01.103.

Hossain, M. M., Tasnim, S., Sultana, A., Faizah, F., Mazumder, H., Zou, L., McKyer, E. L. J., Ahmed, H. U., & Ma, P. (2020). Epidemiology of mental health problems in COVID-19: a review. F1000Research, 9, 636. https://doi.org/10.12688/f1000research.24457.1

Harmer B, Lee S, Duong TVH, & Saadabadi A. (2022) Suicidal ideation. StatPearls [Internet]. StatPearls Publishing

Simon RI. (2014) Passive suicidal ideation: still a high-risk clinical scenario. Current Psychiatry 13(3):13-15

Kasal, A., Táborská, R., Juríková, L., Grabenhofer-Eggerth, A., Pichler, M., Gruber, B., Tomášková, H., & Niederkrotenthaler, T. (2023). Facilitators and barriers to implementation of suicide prevention interventions: Scoping review. Global mental health (Cambridge, England), 10, e15. https://doi.org/10.1017/gmh.2023.9.

Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., Musacchio, K. M., Jaroszewski, A. C., Chang, B. P., & Nock, M. K. (2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological bulletin, 143(2), 187–232. https://doi.org/10.1037/bul0000084.

Berrouiguet, S., Billot, R., Larsen, M. E., Lopez-Castroman, J., Jaussent, I., Walter, M., Lenca, P., Baca-García, E., & Courtet, P. (2019). An Approach for Data Mining of Electronic Health Record Data for Suicide Risk Management: Database Analysis for Clinical Decision Support. JMIR mental health, 6(5), e9766. https://doi.org/10.2196/mental.9766Miller DD, Brown EW. Artificial Intelligence in Medical Practice: The Question to the Answer? Am J Med. 2018 Feb;131(2):129-133. doi: 10.1016/j.amjmed.2017.10.035.

Torous, J., Larsen, M. E., Depp, C., Cosco, T. D., Barnett, I., Nock, M. K., & Firth, J. (2018). Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps. Current psychiatry reports, 20(7), 51. https://doi.org/10.1007/s11920-018-0914-y

Lejeune, A., Le Glaz, A., Perron, P. A., Sebti, J., Baca-Garcia, E., Walter, M., Lemey, C., & Berrouiguet, S. (2022). Artificial intelligence and suicide prevention: a systematic review. European psychiatry : the journal of the Association of European Psychiatrists, 65(1), 1–22. Advance online publication. https://doi.org/10.1192/j.eurpsy.2022.8

Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligence in medicine. Journal of family medicine and primary care, 8(7), 2328–2331. https://doi.org/10.4103/jfmpc.jfmpc_440_19

Boudreaux, E. D., Rundensteiner, E., Liu, F., Wang, B., Larkin, C., Agu, E., Ghosh, S., Semeter, J., Simon, G., & Davis-Martin, R. E. (2021). Applying Machine Learning Approaches to Suicide Prediction Using Healthcare Data: Overview and Future Directions. Frontiers in psychiatry, 12, 707916. https://doi.org/10.3389/fpsyt.2021.707916

Ji, S., Pan, S., Li, X., Cambria, E., Long, G., & Huang, Z. (2021). Suicidal ideation detection: A review of machine learning methods and applications. IEEE Transactions on Computational Social Systems, 8(2), 214–226.

Atmakuru, A., Shahini, A., Chakraborty, S., Seoni, S., Salvi, M., Hafeez-Baig, A., Rashid, S., Tan, R.-S., Barua, P., Molinari, F., & Acharya, U. R. (2024). Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023). Information Fusion, 114 102673.

Ehtemam, H., Sadeghi Esfahlani, S., Sanaei, A., Ghaemi, M. M., Hajesmaeel-Gohari, S., Rahimisadegh, R., Bahaadinbeigy, K., Ghasemian, F., & Shirvani, H. (2024). Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies. BMC medical informatics and decision making, 24(1), 138. https://doi.org/10.1186/s12911-024-02524-0

D'Hotman, D., & Loh, E. (2020). AI enabled suicide prediction tools: a qualitative narrative review. BMJ health & care informatics, 27(3), e100175. https://doi.org/10.1136/bmjhci-2020-100175

Gomes de Andrade, N., Pawson, D., Muriello, D., Donahue L., & Guadagno J., (2018) Ethics and Artificial Intelligence: Suicide Prevention on Facebook. Philosophy & Technology 31, 669–684. https://doi.org/10.1007/s13347-018-0336-0

Vogel L. (2018). AI opens new frontier for suicide prevention. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne, 190(4), E119. https://doi.org/10.1503/cmaj.109-5549

Yeskuatov, E., Chua, S. L., & Foo, L. K. (2022). Leveraging Reddit for Suicidal Ideation Detection: A Review of Machine Learning and Natural Language Processing Techniques. International journal of environmental research and public health, 19(16), 10347. https://doi.org/10.3390/ijerph191610347

Arowosegbe, A., & Oyelade, T. (2023). Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review. International journal of environmental research and public health, 20(2), 1514. https://doi.org/10.3390/ijerph20021514

Zhong, Q. Y., Mittal, L. P., Nathan, M. D., Brown, K. M., Knudson González, D., Cai, T., Finan, S., Gelaye, B., Avillach, P., Smoller, J. W., Karlson, E. W., Cai, T., & Williams, M. A. (2019). Use of natural language processing in electronic medical records to identify pregnant women with suicidal behavior: towards a solution to the complex classification problem. European journal of epidemiology, 34(2), 153–162. https://doi.org/10.1007/s10654-018-0470-0

Sweeney, C., Ennis, E., Mulvenna, M. D., Bond, R., & O'Neill, S. (2024). Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic Review. JMIR mental health, 11, e55747. https://doi.org/10.2196/55747

Holmes, G., Tang, B., Gupta, S., Venkatesh, S., Christensen, H., & Whitton, A. (2025). Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review. Journal of medical Internet research, 27, e63126. https://doi.org/10.2196/63126

Rassy, J., Bardon, C., Dargis, L., Côté, L. P., Corthésy-Blondin, L., Mörch, C. M., & Labelle, R. (2021). Information and Communication Technology Use in Suicide Prevention: Scoping Review. Journal of medical Internet research, 23(5), e25288. https://doi.org/10.2196/25288

Bennett-Poynter, L., Kundurthi, S., Besa, R., Joyce, D. W., Kormilitzin, A., Shen, N., Sunwoo, J., Szkudlarek, P., Sequiera, L., & Sikstrom, L. (2025). Harnessing digital health data for suicide prevention and care: A rapid review. Digital health, 11, 20552076241308615. https://doi.org/10.1177/20552076241308615

Andersson, G., & Titov, N. (2014). Advantages and limitations of Internet-based interventions for common mental disorders. World psychiatry: official journal of the World Psychiatric Association (WPA), 13(1), 4–11. https://doi.org/10.1002/wps.20083

Al-Remawi, M., Ali Agha, A. S. A., Al-Akayleh, F., Aburub, F., & Abdel-Rahem, R. A. (2024). Artificial intelligence and machine learning techniques for suicide prediction: Integrating dietary patterns and environmental contaminants. Heliyon, 10(24), e40925. https://doi.org/10.1016/j.heliyon.2024.e40925