The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Can Artificial Intelligence Chatbots Improve Mental Health?: A Scoping Review
Abstract
Background and Objectives: Mental health disorders, including anxiety and depression, are the leading causes of global health-related burden and have increased dramatically since the 1990s. Delivering mental healthcare using artificial intelligence chatbots may be one option for closing the gaps in mental healthcare access. The overall aim of this scoping review was to describe the use, efficacy, and advantages/disadvantages of using an artificial intelligence chatbot for mental healthcare (stress, anxiety, depression). Methods: PubMed, PsycINFO, CINAHL, and Web of Science databases were searched. When possible, Medical Subject Headings terms were searched in combination with keywords. Two independent reviewers reviewed a total of 5768 abstracts. Results: Fifty-four articles were chosen for further review, with 10 articles included in the final analysis. Regarding quality assessment, the overall quality of the evidence was lower than expected. Overall, most studies showed positive trends in improving anxiety, stress, and depression. Discussion: Overall, using an artificial intelligence chatbot for mental health has some promising effects. However, many studies were done using rudimentary versions of artificial intelligence chatbots. In addition, lack of guardrails and privacy issues were identified. More research is needed to determine the effectiveness of artificial intelligence chatbots and to describe undesirable effects.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Systematic reviews broadly support the potential of CBT-inspired chatbots to assist individuals with mild-to-moderate mental health conditions, though they also call for more rigorous long-term evaluation and clearer clinical governance frameworks (Balcombe, 2023;Casu et al., 2024;Gallegos et al., 2024;Kwon et al., 2024).”
From Artificial intelligence and unconditional love: the rise of generative AI as an alternative form of mental health support (2026) · cites this paper as Gallegos et al., 2024
Citations
Cites
Santomauro, D. F., Mantilla Herrera, A. M., Shadid, J., Zheng, P., Ashbaugh, C., Pigott, D. M., Abbafati, C., Adolph, C., Amlag, J. O., Aravkin, A. Y., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)02143-7 · Cited by 5,444
Elhai, J. D., Dvorak, R. D., Levine, J. C., & Hall, B. J. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2016.08.030 · Cited by 1,689
Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields
Kmet, L. M., Cook, L. S., & Lee, R. C. (2004) · University of Alberta Library · doi:10.7939/r37m04f16 · Cited by 1,682
Inkster, B., Sarda, S., & Subramanian, V. (2018) · doi:10.2196/preprints.12106 · Cited by 1,063
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715
Artificial Intelligence Transforms the Future of Health Care
Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.01.017 · Cited by 626
Privacy-preserving artificial intelligence in healthcare: Techniques and applications
Khalid, N., Qayyum, A., Bilal, M., Al-Fuqaha, A., & Qadir, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106848 · Cited by 581
Review and evaluation of mindfulness-based iPhone apps
Mani, M., Kavanagh, D. J., Hides, L., & Stoyanov, S. R. (2015) · JMIR mHealth and uHealth · doi:10.2196/mhealth.4328 · Cited by 495
Flett, J. A. M., Hayne, H., Riordan, B. C., Thompson, L. M., & Conner, T. S. (2019) · Mindfulness · doi:10.1007/s12671-018-1050-9 · Cited by 338
Mongelli, F., Georgakopoulos, P., & Pato, M. T. (2020) · Focus · doi:10.1176/appi.focus.20190028 · Cited by 331
Flagged first, then by citations
Research works printed but not linked: 16
- 2014, Waiting to see the specialist: patient and provider characteristics of wait times from primary to specialty care, BMC Family Practice, 15, 16, 10.1186/1471-2296-15-16 · DOI
- 2022, Using a tele-behavioral health rapid intake model to address high demand for psychotherapy at an academic medical center during COVID-19, Frontiers in Psychiatry, 13, 989838, 10.3389/fpsyt.2022.989838 · DOI
- 2018, Changes in health insurance coverage and barriers to health care access among individuals with serious psychological distress following the affordable care act, Administration and Policy in Mental Health, 45, 924, 10.1007/s10488-018-0875-9 · DOI · Paper page
- 2020, The association of depression stigma with barriers to seeking mental health care: a cross-sectional analysis, Journal of Mental Health, 29, 182, 10.1080/09638237.2019.1644494 · DOI
- 2019, The digital revolution and its impact on mental health care, Psychology and Psychotherapy, 92, 277, 10.1111/papt.12222 · DOI · Paper page
- 2023, Large language models in medicine, Nature Medicine, 29, 1930, 10.1038/s41591-023-02448-8 · DOI · Paper page
- 2022, Improving well-being with a mobile artificial intelligence-powered acceptance commitment therapy tool: pragmatic retrospective study, JMIR Formative Research, 6, e36018, 10.2196/36018 · DOI
- 2022, Chatbot-delivered cognitive behavioral therapy in adolescents with depression and anxiety during the COVID-19 pandemic: feasibility and acceptability study, JMIR Formative Research, 6, e40242, 10.2196/40242 · DOI · Paper page
- 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, e38067, 10.2196/38067 · DOI
- 2020, Use of the patient health questionnaire (PHQ-9) in practice: interactions between patients and physicians, Qualitative Health Research, 30, 2146, 10.1177/1049732320924625 · DOI
- 2013, Psychometric properties of Positive and Negative Affect Schedule (PANAS) original and short forms in an African American community sample, Journal of Affective Disorders, 151, 942, 10.1016/j.jad.2013.08.011 · DOI
- 2023, Artificial intelligence: an eye cast towards the mental health nursing horizon, International Journal of Mental Health Nursing, 32, 938, 10.1111/inm.13121 · DOI
- 2022, The effectiveness of mobile apps for monitoring and management of suicide crisis: a systematic review of the literature, Journal of Clinical Medicine, 11, 5616, 10.3390/jcm11195616 · DOI
- 2022, The association of abuse and depression with suicidal ideation in Chinese adolescents: a network analysis, Frontiers in Psychiatry, 13, 853951, 10.3389/fpsyt.2022.853951 · DOI
- 2021, The impact of different types of abuse on depression, Depression Research and Treatment, 2021, 6654503
- 2023, Health care privacy risks of AI chatbots, Journal of the American Medical Association, 330, 311, 10.1001/jama.2023.9618 · DOI
Cited by
Sorted by citations
Miller, V., Hill, M. J., & Moreira, T. (2026) · Communication and Change · doi:10.1007/s44382-026-00036-1 · Cited by 2
Metadata
- Authors
- Gallegos, C., Kausler, R., Alderden, J., Davis, M., & Wang, L.
- Venue
- CIN: Computers, Informatics, Nursing
- Published
- 2024-06-26
- Volume
- 42
- Issue
- 10
- Pages
- 731-736
- ISSN
- 1538-9774
- Cited by
- 18
- References
- 18
- Language
- en
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
