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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]
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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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