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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]
Citations
Cites
Merikangas, K. R., He, J.-P., Burstein, M., Swanson, S. A., Avenevoli, S., Cui, L., Benjet, C., Georgiades, K., & Swendsen, J. (2010) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2010.05.017 · Cited by 6,278
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
Gulliver, A., Griffiths, K. M., & Christensen, H. (2010) · BMC Psychiatry · doi:10.1186/1471-244x-10-113 · Cited by 3,292
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · cited 5×
“studies, a common complaint from users is that interactions with chatbots become repetitive [61,85,98,102], which makes”
BackgroundInfluentialWang, P. S., Lane, M., Olfson, M., Pincus, H. A., Wells, K. B., & Kessler, R. C. (2005) · Archives of General Psychiatry · doi:10.1001/archpsyc.62.6.629 · Cited by 2,695
Czeisler, M. É., Lane, R. I., Petrosky, E., Wiley, J. F., Christensen, A., Njai, R., Weaver, M. D., Robbins, R., Facer-Childs, E. R., Barger, L. K., et al. (2020) · MMWR. Morbidity and Mortality Weekly Report · doi:10.15585/mmwr.mm6932a1 · Cited by 2,686
Eysenbach, G. (2005) · Journal of Medical Internet Research · doi:10.2196/jmir.7.1.e11 · Cited by 2,524
Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic
Ettman, C. K., Abdalla, S. M., Cohen, G. H., Sampson, L., Vivier, P. M., & Galea, S. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.19686 · Cited by 2,138
Wang, P. S., Aguilar-Gaxiola, S., Alonso, J., Angermeyer, M. C., Borges, G., Bromet, E. J., Bruffaerts, R., de Girolamo, G., de Graaf, R., Gureje, O., et al. (2007) · The Lancet · doi:10.1016/s0140-6736(07)61414-7 · Cited by 1,655 · cited 1×
“Worldwide, it is estimated that 70% of people with mental illness receive no formal treatment [5].”
BackgroundComputer-based psychological treatments for depression: A systematic review and meta-analysis
Richards, D., & Richardson, T. (2012) · Clinical Psychology Review · doi:10.1016/j.cpr.2012.02.004 · Cited by 1,442
Research works printed but not linked: 19
- Nagata JM, 2021, Psychiatr Serv
- Ahmed A, 2021, Comput Meth Prog Bio, 1, 1
- Sana M, 2017, IJACSA, 8, 9
- Mirheidari B, 2018, Detecting early signs of dementia in conversation [dissertation]
- DeVault D, 2014, Proceedings of the 2014 international conference on Autonomous agents and multi-agent systems (AAMAS ‘14), 1061
- Divya S, 2018, WDJWD, 3, 1
- Sweeney C, 2021, ACM HEALTH, 2, 1
- 10.2196/12106 · DOI · Paper page
- 10.1007/s12668-016-0376-9 · DOI
- Cameron G, 2018, Proceedings of the 5th International Conference on Internet Science, INSCI 2018, 121
- Ghosh S, 2018, Stud Health Technol Inform, 252, 51
- 10.2196/mental.9782 · DOI · Paper page
- Radziwill NM, 2017, arXiv, 21
- Yu Z, Papangelis A, Rudnicky A. TickTock: anon-goal-oriented multimodal dialog system with engagement awareness. AAAI Spring symposium series. Pittsburg: PA; 2015.
- Kamita T, 2019, Mob Inf Syst
- Adamopoulou E, 2020, ML with Appl, 2, 100006
- Montemayor C, 2021, AI Soc, 1
- Fadhil A, 2019, ArXiv
- Lisetti C, 2013, ACM Trans Inf Syst, 4, 1
Cited by
Sorted by citations
Items 301–400 of 442
Weeks, R., Sangha, P., Cooper, L., Sedoc, J., White, S., Gretz, S., Toledo, A., Lahav, D., Hartner, A.-M., Martin, N., et al. (2022) · doi:10.2196/preprints.40533
Tan, T. C., Roslan, N. E., Li, J. W., Zou, X., Chen, X., Ratnasari, R., & Santosa, A. (2023) · doi:10.2196/preprints.49239
An E-Commerce Conversational Virtual Assistant in Service of Mild Cognitive Impairment Patients
Kostis, I.-A., Sarafis, D., Karamitsios, K., Tsolaki, M., & Tsolaki, A. (2023) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc58397.2023.10218226
Zhu, J., Dong, A., Wang, C., Veldhuizen, S., Abdelwahab, M., Brown, A., Selby, P. L., & Rose, J. (2024) · doi:10.2196/preprints.56973
Omoboye, M., & Eneh, N. E. (2024) · World Journal of Advanced Research and Reviews · doi:10.30574/wjarr.2024.21.2.0632
Yang, F., Wei, J., Zhao, X., & An, R. (2024) · doi:10.2196/preprints.58597
Villarreal‐Zegarra, D., García-Serna, J., Quispe-Callo, G., Lázaro-Cruz, G., Centeno-Terrazas, G., Gálvez-Arévalo, R. A., Escobar-Agreda, S., Rodríguez, A. D., Reátegui-Rivera, C. M., & Finkelstein, J. (2024) · doi:10.2196/preprints.59560
Design of a Chatbot for Relieving Academic Stress in Adolescent Students
Malik, A., & Kumar, T. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-1329-5_8
Taher, R., Stahl, D. R., Shergill, S. S., & Yiend, J. (2024) · doi:10.2196/preprints.62974
Aditama, M. H. R., Narosaputra, D. A. N., & Selfiardy, S. (2024) · Psychiatry Research · doi:10.1016/j.psychres.2024.116205
Sava, R. N., Tarcan, S., & Vahia, I. V. (2024) · Oxford University Press eBooks · doi:10.1093/med/9780197617328.003.0010
Scherer, J. N., Oliveira, G. R., Machado, F. M. R., Gomes, C. F., & Ornell, F. R. (2024) · Journal of Psychoactive Drugs · doi:10.1080/02791072.2024.2421921
Lu, J., Yan, W., Cui, R., & Hu, T. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-3120 · Cited by 1
di Bona, K., Ijaz, K., & Ahmadpour, N. (2024) · doi:10.2196/preprints.69833
Steinberg, R. J., Amini, J., Wiliszewski, A., Chan, P. P. M., Khan, Z., Martel, A. L., Starritt, K., Sinyor, M., & Schaffer, A. (2025) · medRxiv · doi:10.1101/2025.02.12.25322159 · Cited by 1
Peng, Y., Liu, Y., & Wang, Y. (2024) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1145/3711129.3711233
Park, J. J., Brown, S. A., Chu, S. L., Zhou, A. Q., & Abreu, R. L. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5153715 · Cited by 1
Dallison, S., Moffett, B., Makhubela, P., Nkuna, T., Pozuelo, J. R., van Heerden, A. C., & O’Mahen, H. (2025) · doi:10.2196/preprints.73535
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Gounder, K. K., Tripathy, B. K., & Wagh, D. A. (2025) · Learning and analytics in intelligent systems · doi:10.1007/978-3-031-82706-8_23
Xu, S. (2025) · Mental Health and Digital Technologies · doi:10.1108/mhdt-01-2025-0004
Reclik, M., Lubecka, E., Kwaśnica, B., Kożuch, A., Łosiewicz, A., & Krysta, K. (2025) · Journal of Technology in Behavioral Science · doi:10.1007/s41347-025-00506-1
Savelidi, M., Savelidis, S., Christoforidis, C., Kleitsas, V., Tzanetopoulou, A., & Fasouraki, R. (2024) · European Journal of Engineering and Technology Research · doi:10.24018/ejeng.2024.1.cie.3237
Singgalen, Y. A. (2024) · Building of Informatics Technology and Science (BITS) · doi:10.47065/bits.v6i2.5846
Wah, J. N. K. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5170507
Al-Akawa, L. M. (2025) · Journal of Information Systems Engineering & Management · doi:10.52783/jisem.v10i45s.8716
Chatbots in psychological screening: A report on our experiences
Souček, R., & Voss, A. S. (2025) · EWOP in Practice · doi:10.21825/ewopinpractice.94696
Accessibility of Medical Information to a Broader User Group: The “Athena” Medical ChatBot
Malonas, C., & Alexandris, C. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93861-0_8
Starovoitenko, O., Slyusarevskyy, M., & Чорна, Л. (2025) · Insight the psychological dimensions of society · doi:10.32999/2663-970x/2025-13-20 · Cited by 4
Cliffe, B., Biddle, L., Gore-Rodney, J., & Linton, M.-J. (2025) · Digital Society · doi:10.1007/s44206-025-00202-w
Socio-emotional Determinants of Students’ Attitude to Artificial Intelligence
Kolek, A. (2024) · The New Educational Review · doi:10.15804/tner.2024.si.5.10
Shaikh, S. N., Zhen, L., Sohu, J. M., Shah, S. A., & Soomro, H. A. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5072-1.ch015
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KUMAR, S. P., Kumar, A., & Prasanna, A. A. (2025) · Global Clinical Engineering Journal · doi:10.31354/globalce.v7i2.229
Chatbots in Higher Education: Supporting Student Engagement and Retention
Abdullah, Z. B. (2025) · Advances in Science, Technology & Innovation/Advances in science, technology & innovation · doi:10.1007/978-3-031-56848-0_9
Nagare, P. V., & Shirke, P. (2025) · AIP conference proceedings · doi:10.1063/5.0289454
Loneliness Re-Examined: Societal Influences and Future Research Directions
Aartsen, M., Suanet, B., Hansen, T., & Precupețu, I. (2025) · International perspectives on aging · doi:10.1007/978-3-031-94565-6_15
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Shi, Q. (2025) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ieeeconf65522.2025.11137008 · Cited by 1
Sharda, D., Bansal, R., Kaur, N., & Chawla, S. (2025) · Cureus Journal of Computer Science. · doi:10.7759/s44389-025-08088-y · Cited by 1
Ufniarski, T., Ufniarska, M., Piech, A., Pasierb, K., Poplicha, K., Grodzińska, M., Siuzdak, B., Moszkowicz, J., Sobkiewicz, P., Kardasz, P., et al. (2025) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.3(47).2025.3809
Stark, M., Schürrer, D., & Knaut, A. (2025) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3717511.3747078
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Sezgın, E., Clarkson, E., Logan, F., Jackson, D. I., Hussain, S.-A., Stokes, J., Bunger, A. C., Brock, G. N., Fosler‐Lussier, E., Kemper, A. R., et al. (2026) · PLoS ONE · doi:10.1371/journal.pone.0337868
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Varma, A., Saraiya, A. S., Tuli, K., Suresh, S., & Srivastava, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-7917-3.ch009 · Cited by 1
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Ghazali, E. M., Mutum, D. S., & Chung, C.-K. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2595310
Punj, R., Jindal, S., Kamal, E. S., & Gupta, G. (2025) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon66527.2025.11281223
Chiauzzi, E. J., Williams, A. A. A., Mariano, T. Y., Pajarito, S., Robinson, A. H., Kirvin-Quamme, A., & Forman‐Hoffman, V. L. (2024) · Figshare · doi:10.6084/m9.figshare.26674502
Dhinagaran, D. A., Martinengo, L., Ho, M.-H. R., Joty, S., Kowatsch, T., Atun, R. A., & Car, L. T. (2022) · doi:10.2196/preprints.38740
Sezgin, E., Booze, A. M., Wisne, L. M., Jackson, D. I., & Skeens, M. A. (2025) · medRxiv · doi:10.64898/2025.12.03.25341557
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Patil, S. G. S. B., Srivastava, A., Kundu, M., Chib, S., Dingankar, S. U., & Singh, J. (2025) · ShodhKosh Journal of Visual and Performing Arts · doi:10.29121/shodhkosh.v6.i5s.2025.6962
Mutiara, M. F. A., Hermawan, N., & Suprayitno, E. A. (2025) · International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM) · doi:10.1109/cenim67940.2025.11326277
Tucker, I. M., Easton, K., & Prestwood, R. (2025) · De Gruyter eBooks · doi:10.1515/9783111247854-010
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Arakkal, M. A., & Thangal, M. R. (2026) · doi:10.1002/9781394429660.ch5
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Varma, A., Saraiya, A. S., Maheshwari, E., Tuli, K., Srivastava, S., & Suresh, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1325-2.ch007
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Morales, I., Marti-Estevez, I., Doval, S., Gutiérrez, J., Fausor, R., Arribas-García, S., Álvarez-Mon, M., & Quintero, J. (2026) · doi:10.2196/preprints.93672
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Lim, H. (2026) · Research Square · doi:10.21203/rs.3.rs-9007476/v1
Masood, F. (2026) · Journal of Psychosocial Oncology · doi:10.1080/07347332.2026.2649035
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Park, S., Song, Y., Cho, S., & Yun, M. H. (2026) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3772363.3798485
Surani, A. U., & Das, S. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790562
Shirvani, M. S., Crowley, J., Peng, C., Liu, J., Chao, T. W.-C., Martinez, S., Brandt, L., Kim, I.-J., & Yoon, D. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790986 · Cited by 3
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Dehankar, P., Chaturvedi, K., & Das, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/407380
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Kapoor, R., Khurana, P., & Narula, S. (2026) · doi:10.1002/9781394347308.ch6
Ahamed, S., Zaman, M., Mashud, M. A. A., Khan, N., Sarker, F., & Mamun, K. A. (2025) · Gender and Technology Conference (GTC) · doi:10.1109/gtc64325.2025.11478191
Zhang, Z., & Tan, X. (2026) · Digital Health · doi:10.1177/20552076261443211 · Cited by 1
Sanna, L., Bolpagni, M., Fietta, V., Carli, S. D., Franzin, M., Gavioli, G., Gios, L., Pardini, S., Nicoletti, A. E., Rizzi, S., et al. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-16708-8_38
Yang, Y., Tavares, J., & Oliveira, T. (2026) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2026.100413
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Resende, J. P., Tayoun, G., Schoving, M.-P., Bouhedec, M. L., Guennec, E., Meradji, S., Jauregi, A., Milhiet, V., & Cohen, D. (2026) · Neuropsychiatrie de l Enfance et de l Adolescence · doi:10.1016/j.neurenf.2026.04.004
Chen, X., Li, Y., & Peng, J. (2026) · International Conference on Artificial Intelligence and Digital Services · doi:10.1145/3803686.3803713
Dong, J., Chen, X., Mu, J., Wu, M., Ruan, C., Cui, W., Yan, F., & ZHANG, D. (2026) · Research Square · doi:10.21203/rs.3.rs-9431785/v1
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Roca, P., Sanchez-Pedreño, M., Rodriguez-Fernandez, G., del Valle, E. P. G., & Zangri, R. M. (2026) · APUNTES DE PSICOLOGÍA · doi:10.70478/apuntes.psi.2026.44.09 · Cited by 1
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Moumni, H., & Elghazouani, F. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18894-6_2
Kholerdi, F. A., & Yahagh, F. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6819858
Yum, S. (2026) · Journal of Public Mental Health · doi:10.1108/jpmh-01-2026-0016 · Cited by 1
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Cai, J., Likvornik, N. F., & Zychlinski, N. (2026) · IISE Transactions · doi:10.1080/24725854.2026.2679528 · Cited by 1
Hatfield, S., & Tamm, J. (2026) · Theoretical and Applied Ergonomics · doi:10.3390/tae2030017
Li, C., Gu, Z. H., Gu, Z., & Yanfen, S. H. E. (2026) · International Conference on Digital Management and Information Technology (DMIT) · doi:10.1145/3808707.3808745
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Darwish, D. A. M., Allah, S. M. A., Ahmed, N. K., & Ali, R. A. (2026) · International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET) · doi:10.1109/icaiset66439.2026.11541353
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An, R., & Lindsey, M. A. (2026) · Springer eBooks · doi:10.1007/978-3-032-18443-6_2
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Brinck, E. A., Soldner, J. L., Kuo, H. J., Sabella, S. A., Landon, T. J., Bernacchio, C. P., & Boland, E. A. (2026) · Behavioral Sciences · doi:10.3390/bs16061038
Yurkiv, H., Booth, R. G., Garnett, A., & McIntyre, A. (2026) · doi:10.2196/preprints.105389
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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