ChatGPT as a Complementary Mental Health Resource: A Boon or a Bane
Open accessAbstract
The launch of Open AI's chatbot, ChatGPT, has generated a lot of attention and discussion among professionals in several fields. Many concerns and challenges have been brought up by researchers from various fields, particularly in relation to the harm that using these tools for medical diagnosis and treatment recommendations can cause. Additionally, it has been debated if ChatGPT is dependable, efficient, and helpful for clinicians and medical professionals. Therefore, in this study, we assess ChatGPT's effectiveness in providing mental health support, particularly for issues related to anxiety and depression, based on the chatbot's responses and cross-questioning. The findings indicate that there are significant inconsistencies and that ChatGPT's reliability is low in this specific domain. As a result, care must be used when using ChatGPT as a complementary mental health resource.
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ChatGPT, in its current iteration (gpt-3.5-turbo-16k), manifests only modest results, confirming its previously reported non-specialized nature for clinical applications.
“ChatGPT, in its current iteration (gpt-3.5-turbo-16k), manifests only modest results, confirming its previously reported non-specialized nature for clinical applications13,22,23.”
From AI and narrative embeddings detect PTSD following childbirth via birth stories (2024) · cites this paper as [23]
The Discussion says ChatGPT should complement, not replace, in-person mental health care, and the Conclusion cautions against using it for medication recommendations. These passages support caution about clinical use, but the paper does not establish that ChatGPT is generally non-specialized for all clinical applications or assess gpt-3.5-turbo-16k. (LLM, conf=0.84)
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Home visits: a strategy to improve newborn survival - Authors' reply
(2008) · Americanae (AECID Library) · doi:10.1016/s0140 · Cited by 22,524 · see citing works
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · see citing works
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 1× · see citing works
“It has been utilized in a variety of fields, including education [10], scientific research [3, 4], and computer programming [9].”
Scale up services for mental disorders: a call for action
(2007) · The Lancet · doi:10.1016/s0140-6736(07)61242-2 · Cited by 835 · see citing works
Use chat gpt to solve programming bugs
Surameery, N. M. S., & Shakor, M. Y. (2023) · International Journal of Information technology and Computer Engineering · doi:10.55529/ijitc.31.17.22 · Cited by 382 · see citing works
Hirosawa, T., Harada, Y., Yokose, M., Sakamoto, T., Kawamura, R., & Shimizu, T. (2023) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph20043378 · Cited by 344 · see citing works
ChatGPT passing USMLE shines a spotlight on the flaws of medical education
Mbakwe, A. B., Lourentzou, I., Celi, L. A., Mechanic, O. J., & Dagan, A. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000205 · Cited by 273 · see citing works
A Chat(GPT) about the future of scientific publishing
Hill-Yardin, E. L., Hutchinson, M. R., Laycock, R., & Spencer, S. J. (2023) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2023.02.022 · Cited by 270 · see citing works
Evaluating the use of large language model in identifying top research questions in gastroenterology
Lahat, A., Shachar, E., Avidan, B., Shatz, Z., Glicksberg, B. S., & Klang, E. (2023) · Scientific Reports · doi:10.1038/s41598-023-31412-2 · Cited by 111 · see citing works
Balas, M., & Ing, E. B. (2023) · JFO Open Ophthalmology · doi:10.1016/j.jfop.2023.100005 · Cited by 110 · see citing works
How trustworthy is ChatGPT? The case of bibliometric analyses
Farhat, F., Sohail, S. S., & Madsen, D. Ø. (2023) · Cogent Engineering · doi:10.1080/23311916.2023.2222988 · Cited by 69 · see citing works
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Research works printed but not linked: 1
- Wainberg, M. L., Scorza, P., Shultz, J. M., Helpman, L., Mootz, J. J., Johnson, K. A., Neria, Y., Bradford, J. M. E., Oquendo, M. A., & Arbuckle, M. R. (2017). Challenges and Opportunities in Global Mental Health: a Research-to-Practice Perspective. Current Psychiatry Reports, 19(5), 1-10. https://doi.org/10.1007/S11920-017- 0780-Z/METRICS · DOI
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Large Language Models for Chatbot Health Advice Studies: A Systematic Review
Huo, B., Boyle, A., Marfo, N., Tangamornsuksan, W., Steen, J. P., McKechnie, T., Lee, Y., Mayol, J., Antoniou, S. A., Thirunavukarasu, A. J., et al. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.57879 · Cited by 283 · see citing works
Large Language Models for Mental Health Applications: Systematic Review
Guo, Z., Lai, A., Thygesen, J. H., Farrington, J., Keen, T., & Li, K. (2024) · JMIR Mental Health · doi:10.2196/57400 · Cited by 269 · see citing works
Assessing the Effectiveness of ChatGPT in Delivering Mental Health Support: A Qualitative Study
Alanezi, F. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s447368 · Cited by 139 · see citing works
AI and narrative embeddings detect PTSD following childbirth via birth stories
Bartal, A., Jagodnik, K. M., Chan, S. J., & Dekel, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-54242-2 · Cited by 45 · see citing works
“5-turbo-16k), manifests only modest results, confirming its previously reported non-specialized nature for clinical applications 13,22,23 .”
A scoping review on the mental health harms of LLM-based chatbots
Diel, A., Torous, J., Cuijpers, P., Kleesiek, J., Nensa, F., Weber, N., Faust, F., Lalgi, T. J., Mellis, F. S., Teufel, M., et al. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-03054-x · Cited by 4 · see citing works
- Authors
- Farhat, F.
- Affiliations
- Aligarh Muslim University (IN)
- Corresponding author
- Farhat, Faiza
- ORCID
- Farhat, Faiza
- Venue
- Annals of Biomedical Engineering
- Publisher
- MDPI AG
- Published
- 2023-07-21
- Volume
- 52
- Issue
- 5
- Pages
- 1111-1114
- ISSN
- 0090-6964 (ISSN-L)
- OpenAlex
- W4384926409
- Cited by
- 76
- References
- 11
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 32 · 2023: 10
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Annals of Biomedical Engineering (publishedVersion)
- Preprints.org (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- ChatGPT, large language models, mental health support, anxiety, depression, chatbot reliability, clinical decision support
- MeSH terms
- Anxiety, Mental Health, Software, Reproducibility of Results
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