The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
AI in mental health
Abstract
With the advent of digital approaches to mental health, modern artificial intelligence (AI), and machine learning in particular, is being used in the development of prediction, detection and treatment solutions for mental health care. In terms of treatment, AI is being incorporated into digital interventions, particularly web and smartphone apps, to enhance user experience and optimise personalised mental health care. In terms of prediction and detection, modern streams of abundant data mean that data-driven AI methods can be employed to develop prediction/detection models for mental health conditions. In particular, an individual's 'digital exhaust', the data gathered from their numerous personal digital device and social media interactions, can be mined for behavioural or mental health insights. Language, long considered a window into the human mind, can now be quantitatively harnessed as data with powerful computer-based natural language processing to also provide a method of inferring mental health. Furthermore, natural language processing can also be used to develop conversational agents used for therapeutic intervention.
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“These tools show potential for AI mental health chatbots; however, critiques have been made about the size of the sample in the validation studies and the lack of research on their long-term efficacy [22].”
From Providing Self-Led Mental Health Support Through an Artificial Intelligence–Powered Chat Bot (Leora) to Meet the Demand of Mental Health Care (2023) · cites this paper as [22]
In the citing paper
“A number of mobile apps for mental health are available in app stores that have incorporated artificial intelligence (AI) and machine learning (ML) technologies into their service [16–18].”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [18]
Citations
Cites
ELIZA—a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1966) · Communications of the ACM · doi:10.1145/365153.365168 · Cited by 4,452
ELIZA — a computer program for the study of natural language communication between man and machine
Weizenbaum, J. (1983) · Communications of the ACM · doi:10.1145/357980.357991 · Cited by 2,807
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · cited 1×
“However, both groups significantly reduced anxiety as measured by the Generalized Anxiety Disorder scale (GAD-7) [44].”
MethodConversational agents in healthcare: a systematic review
Laranjo, L., Dunn, A. G., Tong, H. L., Kocaballi, A. B., Chen, J., Bashir, R., Surian, D., Gallego, B., Magrabi, F., Lau, A. Y. S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy072 · Cited by 1,388 · cited 1×
“The underlying system can be based on a variety of foundations, ranging from a set of simple rule-based responses and keyword matching to sophisticated NLP techniques [35,36].”
BackgroundInkster, B., Sarda, S., & Subramanian, V. (2018) · doi:10.2196/preprints.12106 · Cited by 1,063
Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape
Vaidyam, A. N., Wisniewski, H., Halamka, J. D., Kashavan, M. S., & Torous, J. B. (2019) · The Canadian Journal of Psychiatry · doi:10.1177/0706743719828977 · Cited by 1,048
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802
Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health
Onnela, J.-P., & Rauch, S. L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.7 · Cited by 726
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715
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Research works printed but not linked: 5
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- Inkster, 2018, JMIR Mhealth Uhealth, 6, 10.2196/12106 · DOI · Paper page
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Cited by
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Enhancing mental health with Artificial Intelligence: Current trends and future prospects
Olawade, D. B., Wada, O. Z., Odetayo, A., David-Olawade, A. C., Asaolu, F. T., & Eberhardt, J. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100099 · Cited by 667
Artificially intelligent chatbots in digital mental health interventions: a review
Boucher, E. M., Harake, N. R., Ward, H. E., Stoeckl, S. E., Vargas, J., Minkel, J., Parks, A. C., & Zilca, R. (2021) · Expert Review of Medical Devices · doi:10.1080/17434440.2021.2013200 · Cited by 440
From promise to practice: towards the realisation of AI-informed mental health care
Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251
To chat or bot to chat: Ethical issues with using chatbots in mental health
Coghlan, S., Leins, K., Sheldrick, S., Cheong, M., Gooding, P., & D'Alfonso, S. (2023) · DIGITAL HEALTH · doi:10.1177/20552076231183542 · Cited by 215
Self-citationKhawaja, Z., & Bélisle-Pipon, J.-C. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1278186 · Cited by 214
Metadata
- Authors
- D’Alfonso, S.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- D’Alfonso, Simon
- ORCID
- D’Alfonso, Simon
- Venue
- Current Opinion in Psychology
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 36
- Pages
- 112-117
- ISSN
- 2352-250X (ISSN-L)
- OpenAlex
- W3033626851
- Semantic Scholar
- 56db71460c2742ae968f5030a318c156f9979b14
- Cited by
- 408
- References
- 53
- Citations by year
- 2026: 84 · 2025: 133 · 2024: 85 · 2023: 56 · 2022: 27 · 2021: 16 · 2020: 7
- FWCI
- 4.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Psychology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Keywords
- artificial intelligence in mental health, machine learning, digital mental health interventions, mobile application, online intervention, mental health prediction, mental health detection, social media data, natural language processing, chatbots, therapeutic chatbots
- MeSH terms
- Machine Learning, Artificial Intelligence, Mental Disorders, Communication, Humans, Mental Health
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