The DOI matches its Crossref record; 116 of the 116 references it cites were found in a registry or an index.
Artificially intelligent chatbots in digital mental health interventions: a review
Open accessAbstract
INTRODUCTION: Increasing demand for mental health services and the expanding capabilities of artificial intelligence (AI) in recent years has driven the development of digital mental health interventions (DMHIs). To date, AI-based chatbots have been integrated into DMHIs to support diagnostics and screening, symptom management and behavior change, and content delivery. AREAS COVERED: , serves as a case study for discussion of potential challenges and how these might be addressed, and demonstrates the promise of chatbots as effective, usable, and adoptable within DMHIs. Finally, we discuss ways in which future research can advance the field, addressing topics including perceptions of AI, the impact of individual differences, and implications for privacy and ethics. EXPERT OPINION: Our discussion concludes with a speculative viewpoint on the future of AI in DMHIs, including the use of chatbots, the evolution of AI, dynamic mental health systems, hyper-personalization, and human-like intervention delivery.
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“Relative to chatbots that do not express empathy, chatbots that express empathy lead to more favorable ratings of companionship, i.e., the activities done together are perceived as more enjoyable or exciting (Boucher et al. 2021; Leite et al. 2013), and better mood after experiencing social exclusion (De Gennaro, Krumhuber, and Lucas 2020).”
From AI Companions Reduce Loneliness (2024) · cites this paper as Boucher et al. 2021
In the citing paper
“However, the study of the applications of AI in mental healthcare remains in its infancy (6, 20).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“AI technology can help address these issues by supporting a versatile, scalable, and powerful approach that can analyze and respond to large amounts of data and adapt to individual healthcare needs (6, 17–23).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“Nevertheless, recent studies have shown promising results of AI applications including suicide risk assessment and risk prediction, identification of mental illness predictors, mental health monitoring, psychoeducation and psychotherapy delivery, therapist training, mental healthcare personalization, mental health triage and decision-making, and promoting treatment engagement (6, 17–23).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“However, because of the novelty of this technology, there are several unanswered questions and considerations such as limitations in the interpretation of language, biases in the interaction with patients from diverse backgrounds, and unanswered ethical, patient safety, and health policy considerations (6, 17–23).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“Moreover, despite the accessibility of online mental healthcare interventions, reduced therapist support or guidance in some online programs, have resulted in participants having low levels of engagement with their treatment and a high risk of dropping out (6).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“Additionally, the studies reporting on the use of ML algorithms found significant success in outcome prediction and feature detection to support online therapy delivery (6, 19, 28, 29, 31, 33, 83, 85, 86, 92–94).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“These reviews recognized the novelty of this technology and the scarcity of evidence but noted several potential benefits in terms of providing a low-resource intensity alternative for individuals experiencing less severe mental health symptoms (6, 17).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“For instance, two recent reviews by Boucher et al. (2021) and Abd-alrazaq et al. (2019), reported that chatbots had shown promising applications including care assistance, symptom screening and monitoring, and therapy delivery.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“This presents the interesting possibility of a potential therapeutic relationship between user and chatbot, which is something that self-guided eCBT has not been able to accomplish (6, 17–23, 74).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“The available literature supports the results obtained by this mixed-methods review, showing that AI-augmented interventions such as AI therapy chatbots can be promising, effective and accessible solutions for mental healthcare delivery (6, 17–23).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“As such, the relevant studies highlighted the importance of analyzing the large datasets produced by online mental healthcare interventions and the efficiency and accuracy of AI algorithms which in some cases were comparable to or better than human assessors (6, 19, 28, 29, 31, 33, 83, 85, 86, 92–94).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“Additionally, the scarcity of studies and low quality of evidence, including the lack of a control or adequate placebo group impacts the strength and reliability of the reported outcomes (6, 17, 18).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“Moreover, AI chatbots and language models have been inefficient at interpreting and responding to idioms, colloquialisms, slang, and metaphors.”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“In a global survey of psychiatrists, around 83% of respondents felt that AI would not be able to match the level of empathetic care provided by a professional (6, 98).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“Moreover as mentioned by other reviews in the field, several of the available studies lack an appropriate comparator or control group which can directly impact the observed effect of the AI-augmented interventions and tools reported in the literature (6, 17–23, 52–54, 57, 58, 67, 72).”
From Examining the role of AI technology in online mental healthcare: opportunities, challenges, and implications, a mixed-methods review (2024) · cites this paper as [6]
In the citing paper
“At the same time, a growing body of research shows that interactions with chatbots can contribute to improving their users’ mental health and quality of life, especially if the specific needs of the user match with chatbot capabilities (22, 24, 27, 31, 34, 40, 49–56).”
From Waiting for a digital therapist: three challenges on the path to psychotherapy delivered by artificial intelligence (2023) · cites this paper as [22]
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Metadata
- Authors
- Boucher, E. M., Harake, N. R., Ward, H. E., Stoeckl, S. E., Vargas, J., Minkel, J., Parks, A. C., & Zilca, R.
- Affiliations
- Happify Health, New York, NY, USA
- Corresponding author
- Boucher, Eliane M.
- ORCID
- Venue
- Expert Review of Medical Devices
- Publisher
- Informa UK Limited
- Published
- 2021-12-03
- Volume
- 18
- Issue
- sup1
- Pages
- 37-49
- ISSN
- 1743-4440 (ISSN-L)
- 1745-2422
- OpenAlex
- W4200615999
- Semantic Scholar
- f6fcfe71f09ce04594c98361f88d4e955fb8a831
- Cited by
- 440
- References
- 116
- Citations by year
- 2026: 111 · 2025: 149 · 2024: 111 · 2023: 55 · 2022: 14
- FWCI
- 40.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Expert Review of Medical Devices (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Happify Health
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- chatbots, artificial intelligence, digital mental health interventions, mental health screening, symptom management, behavior change, artificial intelligence adoption, perceptions of AI, individual differences, privacy, ethics, hyper-personalization
- MeSH terms
- Artificial Intelligence, Humans, Mental Health
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