Artificial Intelligence Research in Mental Health: A Bibliometric Evaluation

Yıl 2024, Cilt: 46 Sayı: 4, 627 - 636, 16.07.2024

Emrah Atılgan , Esra Uslu

https://doi.org/10.20515/otd.1435157

Öz

This study aimed to bibliometrically examine artificial intelligence publications in the field of mental health. In the study, 2773 articles were identified with the keywords "mental health and artificial intelligence" in English in the Web of Science database between 1984 and 2024 were examined. Data analysis and graphical presentation were conducted using Bibliometrix Package in R software. The average publication age of the studies was 2.7, and the annual increase rate was 18.36%. The most active country was the United States of America and Chine, the journal with the most publications was the Frontiers in Psychiatry. The subject of machine learning is both the most frequently used and the leading theme in the field. Similarly, Chatbot is among the themes that shape the field. Alzheimer Disease and bipolar disorder were emerging or declining themes. Deep learning, schizophrenia and dementia topics are at the intersection of themes that shape the field, continue to develop, have developed but remained isolated, and are emerging or declining. This study analyzed the performance and scope of AI studies in the field of mental health using bibliometric data. The results can guide informatics and mental health professionals interested in the subject in their studies.

Anahtar Kelimeler

Artificial Intelligence, Bibliometric Analysis, Mental Health

Ruh Sağlığı Alanında Yapay Zeka Araştırmaları: Bibliyometrik Bir Değerlendirme

Öz

Bu çalışmada ruh sağlığı alanındaki yapay zeka yayınlarının bibliyometrik olarak incelenmesi amaçlanmıştır. Çalışmada 1984 ile 2024 yılları arasında Web of Science veri tabanında İngilizce "mental health and artificial intelligence- ruh sağlığı ve yapay zeka" anahtar kelimeleri ile belirlenen 2773 makale incelenmiştir. Veri analizi ve grafiksel sunumlar, R yazılımındaki Bibliometrix Paketi kullanılarak yapılmıştır. Çalışmaların ortalama yayın yaşı 2,7 ve yıllık artış oranı %18,36 olarak belirlenmiştir. En aktif ülke Amerika Birleşik Devletleri ve Çin olup, en fazla yayın yapan dergi Frontiers in Psychiatry'dir. Makine öğrenimi konusu, alanın hem en sık kullanılanı hem de öncü temasıdır. Benzer şekilde, Chatbot, alanı şekillendiren temalar arasında yer almaktadır. Alzheimer Hastalığı ve bipolar bozukluk, ortaya çıkan veya kaybolan temalar (emerging or declining themes) arasındadır. Derin öğrenme, şizofreni ve demans konuları, alanı şekillendiren temaların gelişmeye devam etmekte, gelişmiş ancak izole kalmış, ortaya çıkan veya kaybolmaya başlayanların kesişim noktasında bulunmaktadır. Bu çalışma ile bibliyometrik verileriler kullanarak ruh sağlığı alanındaki YZ çalışmalarının performansı ve kapsamı analiz edilmiştir. Sonuçlar, konuya ilgi duyan bilgi teknolojileri ve ruh sağlığı profesyonellerine çalışmalarında rehberlik edebilir.

Anahtar Kelimeler

Yapay Zeka, Bibliyometrik Analiz, Ruh Sağlığı

Kaynakça

• 1. Jimma BL. Artificial intelligence in healthcare: A bibliometric analysis. Telemat Informatics Reports [Internet]. 2023 ;9:100041.
• 2. D’Alfonso S. AI in mental health. Curr Opin Psychol [Internet]. 2020 ;36:112–7.
• 3. Zhang C, Lu Y. Study on artificial intelligence: The state of the art and future prospects. J Ind Inf Integr [Internet]. 2021 ;23:100224.
• 4. Secinaro S, Calandra D, Secinaro A, Muthurangu V, Biancone P. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak [Internet]. 2021 ;21(1):125.
• 5. Borges AFS, Laurindo FJB, Spínola MM, Gonçalves RF, Mattos CA. The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. Int J Inf Manage [Internet]. 2021 ;57:102225.
• 6. Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, et al. Artificial intelligence: A powerful paradigm for scientific research. Innov [Internet]. 2021;2(4):100179.
• 7. Ćosić K, Popović S, Šarlija M, Kesedžić I, Jovanovic T. Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers. Croat Med J [Internet]. 2020 ;61(3):279–88.
• 8. Becker A. Artificial intelligence in medicine: What is it doing for us today? Heal Policy Technol [Internet]. 2019 ;8(2):198–205.
• 9. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol [Internet]. 2017;2(4):230–43.
• 10. Guo Y, Hao Z, Zhao S, Gong J, Yang F. Artificial intelligence in health care: Bibliometric analysis. Vol. 22, Journal of Medical Internet Research. 2020.
• 11. Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim H-C, et al. Artificial Intelligence for Mental Health and Mental Illnesses: an Overview. Curr Psychiatry Rep [Internet]. 2019 ;21(11):116.
• 12. Lee EE, Torous J, De Choudhury M, Depp CA, Graham SA, Kim H-C, et al. Artificial Intelligence for Mental Health Care: Clinical Applications, Barriers, Facilitators, and Artificial Wisdom. Biol Psychiatry Cogn Neurosci Neuroimaging [Internet]. 2021 ;6(9):856–64.
• 13. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res [Internet]. 2021 ;133:285–96.
• 14. Ellegaard O, Wallin JA. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics [Internet]. 2015 ;105(3):1809–31.
• 15. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J Informetr [Internet]. 2017 11(4):959–75.
• 16. Bibliometrix [Internet]. Knowledge Synthesis 3 Structures. 2023 [cited 2024 Feb 10].
• 17. Jain J, Walia N, Singh S, Jain E. Mapping the field of behavioural biases: a literature review using bibliometric analysis. Manag Rev Q [Internet]. 2022 ;72(3):823–55.
• 18. Donthu N, Kumar S, Pandey N, Lim WM. Research Constituents, Intellectual Structure, and Collaboration Patterns in Journal of International Marketing : An Analytical Retrospective. J Int Mark [Internet]. 2021 ;29(2):1–25.
• 19. Rejeb A, Rejeb K, Abdollahi A, Treiblmaier H. The big picture on Instagram research: Insights from a bibliometric analysis. Telemat Informatics [Internet]. 2022;73:101876.
• 20. Thormundsson B. Private investments in artificial intelligence (AI) in 2020, by geographical area [Internet]. Statista.com. 2023 [cited 2024 Feb 24].
• 21. Müller AM, Alley S, Schoeppe S, Vandelanotte C. The effectiveness of e-& mHealth interventions to promote physical activity and healthy diets in developing countries: A systematic review. Int J Behav Nutr Phys Act [Internet]. 2016;13(1):109.
• 22. Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med [Internet]. 2019;49(09):1426–48.
• 23. Baminiwatta A. Global trends of machine learning applications in psychiatric research over 30 years: A bibliometric analysis. Asian J Psychiatr [Internet]. 2022;69:102986.
• 24. Iyortsuun NK, Kim S-H, Jhon M, Yang H-J, Pant S. A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis. Healthcare [Internet]. 2023;11(3):285.
• 25. Han J, Zhang Z, Mascolo C, Andre E, Tao J, Zhao Z, et al. Deep Learning for Mobile Mental Health: Challenges and recent advances. IEEE Signal Process Mag [Internet]. 2021 ;38(6):96–105.
• 26. Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform [Internet]. 2018 ;19(6):1236–46.
• 27. Denecke K, Abd-Alrazaq A, Househ M. Artificial Intelligence for Chatbots in Mental Health: Opportunities and Challenges. In: Househ M, Borycki E, Kushniruk A, editors. Multiple Perspectives on Artificial Intelligence in Healthcare Lecture Notes in Bioengineering [Internet]. Springer; 2021. p. 115–28.
• 28. Athota L, Shukla VK, Pandey N, Rana A. Chatbot for Healthcare System Using Artificial Intelligence. In: 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) [Internet]. IEEE; 2020. p. 619–22.
• 29. Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, et al. Conversational agents in healthcare: a systematic review. J Am Med Informatics Assoc [Internet]. 2018 ;25(9):1248–58.
• 30. Gamble A. Artificial intelligence and mobile apps for mental healthcare: a social informatics perspective. Aslib J Inf Manag [Internet]. 2020;72(4):509–23.
• 31. Tyagi A, Singh VP, Gore MM. Towards artificial intelligence in mental health: a comprehensive survey on the detection of schizophrenia. Multimed Tools Appl [Internet]. 2023 ;82(13):20343–405.
• 32. Xie B, Tao C, Li J, Hilsabeck RC, Aguirre A. Artificial Intelligence for Caregivers of Persons With Alzheimer’s Disease and Related Dementias: Systematic Literature Review. JMIR Med Informatics [Internet]. 2020 ;8(8):e18189. 33. Sánchez-Morla EM, Fuentes JL, Miguel-Jiménez JM, Boquete L, Ortiz M, Orduna E, et al. Automatic Diagnosis of Bipolar Disorder Using Optical Coherence Tomography Data and Artificial Intelligence. J Pers Med [Internet]. 2021 18;11(8):803.
• 34. Schneider H. Artificial Intelligence in Schizophrenia. In: Artificial Intelligence in Medicine [Internet]. Cham: Springer International Publishing; 2022. p. 1595–608.
• 35. Richardson A, Robbins CB, Wisely CE, Henao R, Grewal DS, Fekrat S. Artificial intelligence in dementia. Curr Opin Ophthalmol [Internet]. 2022 ;33(5):425–31.