Abstract
Numerous brain illnesses are effectively diagnosed using artificial intelligence (AI). The diagnosis of mental health issues holds significant promise for AI technologies. This study tries to summarize findings from earlier systematic studies on the efficacy of AI models in detecting mental illnesses. It examines AI chatbots as they are offered through Smartphone Mental Health Applications (SMHApps), with a focus on potential societal effects. Additionally, this study contributes to expanding the body of information science-based studies on how AI can support mental health. In the literature survey, the use of AI in healthcare has been discussed and investigated. For AI chatbots and other SMHApps to be effective, society must reject techno-fundamentalism in its approach to AI for mental health and put controls on them. The paper examines the target demographics and how machine learning algorithms can be used to develop effective models, and it offers suggestions for future research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abd-Alrazaq A et al (2022) The performance of artificial intelligence-driven technologies in diagnosing mental disorders: an umbrella review. NPJ Digital Med 5.1:1–12
Gamble A (2020) Artificial intelligence and mobile apps for mental healthcare: a social informatics perspective. Aslib J Inf Manag 72(4):509–523
Sharma M et al (2022) Artificial intelligence applications in health care practice: scoping review. J Med Internet Res 24:10 e40238
Danieli M et al (2022) Assessing the impact of conversational artificial intelligence in the treatment of stress and anxiety in aging adults: randomized controlled trial. JMIR Mental Health 9.9:e38067
https://swisscognitive.ch/2022/07/26/how-ai-can-help-mental-health/
Graham S, Depp C, Lee EE, Nebeker C, Tu X, Kim H-C, Jeste DV (2019). Artificial intelligence for mental health and mental illnesses: an overview. Current Psychiatry Rep 21(11):1—18
Ahmed A, Sultana R, Ullas MTR, Begom M, Rahi MMI, Alam MA (2020) A machine learning approach to detect depression and anxiety using supervised learning. In: 2020 IEEE Asia-Pacific conference on computer science and data engineering (CSDE). IEEE, pp 1–6
Hu Y et al (2022) A dual-stage pseudo-labeling method for the diagnosis of mental disorder on MRI scans. In 2022 International joint conference on neural networks (IJCNN). IEEE
Wu MJ, Passos IC, Bauer IE, Lavagnino L, Cao B, Zunta-Soares GB, Soares JC (2016) Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning. J Affect Disord 192:219–225
Srinivasagopalan S, Barry J, Gurupur V, Thankachan S (2019) A deep learning approach for diagnosing schizophrenic patients. J Exp Theor Artif Intell 31(6):803–816
Papini S et al (2018) Ensemble machine learning prediction of posttraumatic stress disorder screening status after emergency room hospitalization. J Anxiety Disorders 60:35–42
Tate AE, McCabe RC, Larsson H, Lundström S, Lichtenstein P, Kuja-Halkola R (2020) Predicting mental health problems in adolescence using machine learning techniques. PLoS ONE 15(4). Article ID e0230389
https://www.weforum.org/agenda/2021/12/ai-mental-health-cbt-therapy/
https://www.kaggle.com/datasets/osmi/mental-health-in-tech-survey
https://www.kaggle.com/code/michaelacorley/unemployment-and-mental-illness-analysis/data
https://www.kaggle.com/datasets/shariful07/student-mental-health
Inez M‐G, Kasanova Z, Vaessen T, Vachon H, Kirtley O, Viechtbauer W, Reininghaus U Experience sampling methodology in mental health research: new insights and technical developments
https://towardsdatascience.com/neural-network-algorithms-learn-how-to-train-ann-736dab9e6299
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Manek, A.S., Priyanga, P., Christa, S., Dawda, N. (2024). Role of AI and Machine Learning in Mental Healthcare. In: Mishra, D., Yang, X.S., Unal, A., Jat, D.S. (eds) Data Science and Big Data Analytics. IDBA 2023. Data-Intensive Research. Springer, Singapore. https://doi.org/10.1007/978-981-99-9179-2_4
Download citation
DOI: https://doi.org/10.1007/978-981-99-9179-2_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9178-5
Online ISBN: 978-981-99-9179-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)