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.
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
“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
Internet-Based and Other Computerized Psychological Treatments for Adult Depression: A Meta-Analysis
Andersson, G., & Cuijpers, P. (2009) · Cognitive Behaviour Therapy · doi:10.1080/16506070903318960 · Cited by 1,368
Firth, J., Torous, J., Nicholas, J., Carney, R., Pratap, A., Rosenbaum, S., & Sarris, J. (2017) · World Psychiatry · doi:10.1002/wps.20472 · Cited by 1,097
Borghouts, J., Eikey, E., Mark, G., De Leon, C., Schueller, S. M., Schneider, M., Stadnick, N., Zheng, K., Mukamel, D., & Sorkin, D. H. (2021) · Journal of Medical Internet Research · doi:10.2196/24387 · Cited by 1,069 · cited 1×
“culties with using the program or remembering to engage with it [40].”
BackgroundInkster, B., Sarda, S., & Subramanian, V. (2018) · doi:10.2196/preprints.12106 · Cited by 1,063
Trust in the health care professional and health outcome: A meta-analysis
Birkhäuer, J., Gaab, J., Kossowsky, J., Hasler, S., Krummenacher, P., Werner, C. P., & Gerger, H. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170988 · Cited by 964
Barriers to mental health treatment: results from the National Comorbidity Survey Replication
Mojtabai, R., Olfson, M., Sampson, N. A., Jin, R., Druss, B., Wang, P. S., Wells, K. B., Pincus, H. A., & Kessler, R. C. (2011) · Psychological Medicine · doi:10.1017/s0033291710002291 · Cited by 951
A Systematic Review of the Impact of Adherence on the Effectiveness of e-Therapies
Donkin, L., Christensen, H., Naismith, S. L., Neal, B., Hickie, I. B., & Glozier, N. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1772 · Cited by 876
Linardon, J., Cuijpers, P., Carlbring, P., Messer, M., & Fuller‐Tyszkiewicz, M. (2019) · World Psychiatry · doi:10.1002/wps.20673 · Cited by 860
Lattie, E. G., Adkins, E. C., Winquist, N., Stiles-Shields, C., Wafford, Q. E., & Graham, A. K. (2019) · Journal of Medical Internet Research · doi:10.2196/12869 · Cited by 843
From Eliza to XiaoIce: Challenges and opportunities with social chatbots
Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.1700826 · Cited by 749 · cited 1×
“User input is primarily written, via open text or multiple-choice options, while output generated by the chatbot can be written, spoken, or visual [69].”
BackgroundHarnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health
Onnela, J.-P., & Rauch, S. L. (2016) · Neuropsychopharmacology · doi:10.1038/npp.2016.7 · Cited by 726
Fulmer, R., Joerin, A., Gentile, B., Lakerink, L., & Rauws, M. (2018) · doi:10.2196/preprints.9782 · Cited by 715
Firth, J., Torous, J., Nicholas, J., Carney, R., Rosenbaum, S., & Sarris, J. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.04.046 · Cited by 675 · cited 1×
“Meta-analyses of DMHIs show small to medium effects on depression and anxiety symptoms [27–30], with larger effects when programs include more engagement features [31], though other meta-analyses found significant pooled effects for depression but not for other psychological outcomes, including…”
BackgroundThe Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review
Milne-Ives, M., de Cock, C., Lim, E., Shehadeh, M. H., de Pennington, N., Mole, G., Normando, E., & Meinert, E. (2020) · Journal of Medical Internet Research · doi:10.2196/20346 · Cited by 626
An overview of the features of chatbots in mental health: A scoping review
Abd-alrazaq, A. A., Alajlani, M., Alalwan, A. A., Bewick, B. M., Gardner, P., & Househ, M. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.103978 · Cited by 605
Internet-Delivered Psychological Treatments
Andersson, G. (2016) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-021815-093006 · Cited by 595
County-level estimates of mental health professional shortage in the United States.
Thomas, K., Ellis, A. R., Konrad, T., Holzer, C., & Morrissey, J. (2009) · Psychiatric Services · doi:10.1176/ps.2009.60.10.1323 · Cited by 589
Mental health treatment seeking among older adults with depression: The impact of stigma and race
Conner, K. O., Copeland, V. C., Grote, N. K., Koeske, G., Rosen, D., Reynolds, C. F., & Brown, C. (2010) · The American Journal of Geriatric Psychiatry · doi:10.1097/jgp.0b013e3181cc0366 · Cited by 568 · cited 2×
“While some individuals do not seek treatment because of low perceived need [6] or attitudinal barriers like perceived stigma [7],”
BackgroundUsing the Internet to provide cognitive behaviour therapy
Andersson, G. (2009) · Behaviour Research and Therapy · doi:10.1016/j.brat.2009.01.010 · Cited by 560
Internet-administered cognitive behavior therapy for health problems: a systematic review
Cuijpers, P., van Straten, A., & Andersson, G. (2008) · Journal of Behavioral Medicine · doi:10.1007/s10865-007-9144-1 · Cited by 555
Abd-Alrazaq, A. A., Rababeh, A., Alajlani, M., Bewick, B. M., & Househ, M. (2020) · Journal of Medical Internet Research · doi:10.2196/16021 · Cited by 503 · cited 2×
“In regards to anxiety, results are more conflicting [107].”
ResultEstablishing the computer-patient working alliance in automated health behavior change interventions
Bickmore, T., Gruber, A., & Picard, R. (2005) · Patient Education and Counseling · doi:10.1016/j.pec.2004.09.008 · Cited by 494
Standalone smartphone apps for mental health—a systematic review and meta-analysis
Weisel, K. K., Fuhrmann, L. M., Berking, M., Baumeister, H., Cuijpers, P., & Ebert, D. D. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0188-8 · Cited by 447
Mobile Apps for Bipolar Disorder: A Systematic Review of Features and Content Quality
Nicholas, J., Larsen, M. E., Proudfoot, J., & Christensen, H. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4581 · Cited by 438
Cunningham, P. J. (2009) · Health Affairs · doi:10.1377/hlthaff.28.3.w490 · Cited by 420
D’Alfonso, S. (2020) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2020.04.005 · Cited by 408
Physicians’ perceptions of chatbots in health Care: cross-sectional web-based survey
Palanica, A., Flaschner, P., Thommandram, A., Li, M., & Fossat, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/12887 · Cited by 384 · cited 8×
“trepidation about using AI in domains like diagnostics, counseling, and delivering CBT [80,114].”
BackgroundMethodResultInfluentialLy, K. H., Ly, A.-M., & Andersson, G. (2017) · Internet Interventions · doi:10.1016/j.invent.2017.10.002 · Cited by 368
Making conversations with chatbots more personalized
Shumanov, M., & Johnson, L. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2020.106627 · Cited by 363
Torous, J., & Roberts, L. W. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0262 · Cited by 362
Gerke, S., Babic, B., Evgeniou, T., & Cohen, I. G. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0262-2 · Cited by 359
Mental health: there’s an app for that
Anthes, E. (2016) · Nature · doi:10.1038/532020a · Cited by 333
Towards a consensus around standards for smartphone apps and digital mental health
Torous, J., Andersson, G., Bertagnoli, A., Christensen, H., Cuijpers, P., Firth, J., Haim, A., Hsin, H., Hollis, C., Lewis, S., et al. (2019) · World Psychiatry · doi:10.1002/wps.20592 · Cited by 325
Survey of conversational agents in health
Montenegro, J. L. Z., da Costa, C. A., & da Rosa Righi, R. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.03.054 · Cited by 322
Reporting Mental Health Symptoms: Breaking Down Barriers to Care with Virtual Human Interviewers
Lucas, G. M., Rizzo, A., Gratch, J., Scherer, S., Stratou, G., Boberg, J., & Morency, L.-P. (2017) · Frontiers in Robotics and AI · doi:10.3389/frobt.2017.00051 · Cited by 308
Pursuing happiness in everyday life: The characteristics and behaviors of online happiness seekers.
Parks, A. C., Porta, M. D. D., Pierce, R. S., Zilca, R., & Lyubomirsky, S. (2012) · Emotion · doi:10.1037/a0028587 · Cited by 299
Self-citationA systematic assessment of smartphone tools for suicide prevention
Larsen, M. E., Nicholas, J., & Christensen, H. (2016) · PLoS ONE · doi:10.1371/journal.pone.0152285 · Cited by 289
Kretzschmar, K., Tyroll, H., Pavarini, G., Manzini, A., Singh, I., & NeurOx Young People’s Advisory Group (2019) · Biomedical Informatics Insights · doi:10.1177/1178222619829083 · Cited by 270
Use of Digital Mental Health for Marginalized and Underserved Populations
Schueller, S. M., Hunter, J. F., Figueroa, C., & Aguilera, A. (2019) · Current Treatment Options in Psychiatry · doi:10.1007/s40501-019-00181-z · Cited by 268
Greer, S., Ramo, D., Chang, Y.-J., Fu, M., Moskowitz, J., & Haritatos, J. (2019) · JMIR mHealth and uHealth · doi:10.2196/15018 · Cited by 265
Effectiveness of an Empathic Chatbot in Combating Adverse Effects of Social Exclusion on Mood
de Gennaro, M., Krumhuber, E. G., & Lucas, G. (2020) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.03061 · Cited by 265
Hoermann, S., McCabe, K. L., Milne, D. N., & Calvo, R. A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7023 · Cited by 263
Darcy, A., Daniels, J., Salinger, D., Wicks, P., & Robinson, A. (2021) · JMIR Formative Research · doi:10.2196/27868 · Cited by 260
Prochaska, J. J., Vogel, E. A., Chieng, A., Kendra, M., Baiocchi, M., Pajarito, S., & Robinson, A. (2021) · Journal of Medical Internet Research · doi:10.2196/24850 · Cited by 257 · cited 1×
“for depression or anxiety [57], though chatbots have also been developed to support patients with autism [58], suicide risk [59], substance abuse [60], post-traumatic stress disorder, stress [61], dementia [62], and acrophobia [63], and to”
BackgroundArtificial intelligence and the future of psychiatry: Insights from a global physician survey
Doraiswamy, P. M., Blease, C., & Bodner, K. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101753 · Cited by 253
Bickmore, T. W., Pfeifer, L. M., Byron, D., Forsythe, S., Henault, L. E., Jack, B. W., Silliman, R., & Paasche-Orlow, M. K. (2010) · Journal of Health Communication · doi:10.1080/10810730.2010.499991 · Cited by 249
Akbar, S., Coiera, E. W., & Magrabi, F. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz175 · Cited by 246
Ebert, D. D., Cuijpers, P., Muñoz, R. F., & Baumeister, H. (2017) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2017.00116 · Cited by 244
Ecological momentary interventions for mental health: A scoping review
Balaskas, A., Schueller, S. M., Cox, A. L., & Doherty, G. (2021) · PLOS ONE · doi:10.1371/journal.pone.0248152 · Cited by 235 · cited 1×
“intervention and helping to reduce the likelihood of future problems [81].”
BackgroundResponse to a relational agent by hospital patients with depressive symptoms
Bickmore, T. W., Mitchell, S. E., Jack, B. W., Paasche-Orlow, M. K., Pfeifer, L. M., & O’Donnell, J. (2010) · Interacting with Computers · doi:10.1016/j.intcom.2009.12.001 · Cited by 218
Sander, L., Rausch, L., & Baumeister, H. (2016) · JMIR Mental Health · doi:10.2196/mental.6061 · Cited by 218
Bendig, E., Erb, B., Schulze-Thuesing, L., & Baumeister, H. (2022) · Verhaltenstherapie · doi:10.1159/000501812 · Cited by 214
Lee, M., Ackermans, S., van As, N., Chang, H.-W., Lucas, E., & IJsselsteijn, W. A. (2019) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300932 · Cited by 208
Morris, R. R., Schueller, S. M., & Picard, R. W. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4167 · Cited by 206
The digital placebo effect: mobile mental health meets clinical psychiatry
Torous, J., & Firth, J. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00565-9 · Cited by 200
Klos, M. C., Escoredo, M., Joerin, A., Lemos, V. N., Rauws, M., & Bunge, E. L. (2021) · JMIR Formative Research · doi:10.2196/20678 · Cited by 200 · cited 1×
“in the control group, who read a psychoeducational book on depression [109].”
BackgroundLee, Y., Yamashita, N., & Huang, Y. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3392836 · Cited by 195
Demographic Differences in Adolescents' Health Concerns and Perceptions of Helping Agents
Dubow, E. F., Lovko, K. R., & Kausch, D. F. (1990) · Journal of Clinical Child Psychology · doi:10.1207/s15374424jccp1901_6 · Cited by 176
Lau, N., O'Daffer, A., Colt, S., Yi-Frazier, J. P., Palermo, T. M., McCauley, E., & Rosenberg, A. R. (2020) · JMIR mhealth and uhealth · doi:10.2196/17798 · Cited by 174
Wu, A., Scult, M. A., Barnes, E. D., Betancourt, J. A., Falk, A. E., & Gunning, F. M. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00386-8 · Cited by 174
A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis
Baker, A., Perov, Y., Middleton, K., Baxter, J., Mullarkey, D., Sangar, D., Butt, M., DoRosario, A., & Johri, S. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.543405 · Cited by 167
Mohr, D. C., Duffecy, J., Ho, J., Kwasny, M., Cai, X., Burns, M. N., & Begale, M. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0070086 · Cited by 158
Gerhards, S. A. H., Abma, T. A., Arntz, A., de Graaf, L. E., Evers, S. M. A. A., Huibers, M. J. H., & Widdershoven, G. A. M. (2011) · Journal of Affective Disorders · doi:10.1016/j.jad.2010.09.012 · Cited by 141 · cited 1×
“Given that the lack of accountability in unsupported DMHIs is often cited as a drawback [39], one important benefit to chatbots is increased accountability, which leads to increased user engagement [103,104] and behavioral intentions to use”
BackgroundClarke, J., Proudfoot, J., Birch, M.-R., Whitton, A. E., Parker, G., Manicavasagar, V., Harrison, V., Christensen, H., & Hadzi-Pavlovic, D. (2014) · BMC Psychiatry · doi:10.1186/s12888-014-0272-1 · Cited by 138 · cited 1×
“efficacy, these individuals may benefit more from DMHIs in terms of depression, anxiety, and overall distress [125].”
BackgroundPerski, O., Crane, D., Beard, E., & Brown, J. (2019) · DIGITAL HEALTH · doi:10.1177/2055207619880676 · Cited by 135
Feijt, M. A., de Kort, Y. A., Bongers, I. M., & IJsselsteijn, W. A. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9485 · Cited by 134 · cited 1×
“In this case, chatbots could help maintain benefits from treatment by reminding clients of skills and practices (e.g. medication adherence, check-ups, exercise, etc.) [93,97].”
BackgroundTechnology and mental health: The role of artificial intelligence
Lovejoy, C. A. (2019) · European Psychiatry · doi:10.1016/j.eurpsy.2018.08.004 · Cited by 129
Health Chatbots for Fighting COVID-19: a Scoping Review
Almalki, M., & Azeez, F. K. (2020) · Acta Informatica Medica · doi:10.5455/aim.2020.28.241-247 · Cited by 129
Jungmann, S. M., Klan, T., Kuhn, S., & Jungmann, F. (2019) · JMIR Formative Research · doi:10.2196/13863 · Cited by 127
Burton, C., Szentagotai Tatar, A., McKinstry, B., Matheson, C., Matu, S., Moldovan, R., Macnab, M., Farrow, E., David, D., Pagliari, C., et al. (2016) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15609793 · Cited by 125
Gabrielli, S., Rizzi, S., Bassi, G., Carbone, S., Maimone, R., Marchesoni, M., & Forti, S. (2021) · JMIR mHealth and uHealth · doi:10.2196/27965 · Cited by 125
Gaffney, H., Mansell, W., Edwards, R., & Wright, J. (2014) · Behavioural and Cognitive Psychotherapy · doi:10.1017/s135246581300060x · Cited by 122
Blease, C., Locher, C., Leon-Carlyle, M., & Doraiswamy, M. (2020) · DIGITAL HEALTH · doi:10.1177/2055207620968355 · Cited by 121
Web-Based Intervention Programs for Depression: A Scoping Review and Evaluation
Renton, T., Tang, H., Ennis, N., Cusimano, M. D., Bhalerao, S., Schweizer, T. A., & Topolovec-Vranic, J. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3147 · Cited by 116
Abd-Alrazaq, A. A., Bewick, B. M., Farragher, T. M., & Gardner, P. H. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.03.014 · Cited by 113
Harvey, H. B., & Gowda, V. (2020) · Academic Radiology · doi:10.1016/j.acra.2019.09.017 · Cited by 112
Digital Mental Health: The Answer to the Global Mental Health Crisis?
Rudd, B. N., & Beidas, R. S. (2020) · JMIR Mental Health · doi:10.2196/18472 · Cited by 81
Valtolina, S., & Hu, L. (2021) · Biannual Conference of the Italian SIGCHI Chapter (CHItaly) · doi:10.1145/3464385.3464726 · Cited by 78
Christensen, H. M., Batterham, P. J., Mackinnon, A. J., Griffiths, K. M., Hehir, K. K., Kenardy, J. A., Gosling, J., & Bennett, K. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3507 · Cited by 74
Oxygenation of frog gastric mucosa in vitro
Gw, K., & Montgomery, C. (1975) · American Journal of Physiology-Legacy Content · doi:10.1152/ajplegacy.1975.229.6.1510 · Cited by 73
Chatbot-Based Assessment of Employees’ Mental Health: Design Process and Pilot Implementation
Hungerbuehler, I., Daley, K., Cavanagh, K., Garcia Claro, H., & Kapps, M. (2021) · JMIR Formative Research · doi:10.2196/21678 · Cited by 73
Best Practices for Designing Chatbots in Mental Healthcare – A Case Study on iHelpr
Cameron, G., Cameron, D., Megaw, G., Bond, R., Mulvenna, M. D., O’Neill, S. M., Armour, C., & McTear, M. (2018) · Electronic workshops in computing · doi:10.14236/ewic/hci2018.129 · Cited by 70
Price, W. (2017) · Michigan Law Review · doi:10.36644/mlr.116.3.regulating · Cited by 64
Towards trustable machine learning
(2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0315-x · Cited by 64
Davis, C. R., Murphy, K. J., Curtis, R., & Maher, C. A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17239137 · Cited by 63
Development and validation of a virtual agent to screen tobacco and alcohol use disorders
Auriacombe, M., Moriceau, S., Serre, F., Denis, C., Micoulaud‐Franchi, J., de Sevin, É., Bonhomme, É., Bioulac, S., Fatseas, M., & Philip, P. (2018) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2018.08.025 · Cited by 55
Advancing digital health: FDA innovation during COVID-19
Kadakia, K. T., Patel, B., & Shah, A. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00371-7 · Cited by 54
Ferwerda, M., van Beugen, S., van Burik, A., van Middendorp, H., de Jong, E. M. G. J., van de Kerkhof, P. C. M., van Riel, P. L. C. M., & Evers, A. W. M. (2013) · Clinical Rheumatology · doi:10.1007/s10067-013-2175-9 · Cited by 51
TeenChat: A Chatterbot System for Sensing and Releasing Adolescents’ Stress
Huang, J., Li, Q., Xue, Y., Cheng, T., Xu, S., Jia, J., & Feng, L. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-19156-0_14 · Cited by 49
Abashev, A., Grigoryev, R. A., Grigorian, K., & Boyko, V. (2016) · BioNanoScience · doi:10.1007/s12668-016-0376-9 · Cited by 45
Self-help robots drive blues away
Sachan, D. (2018) · The Lancet Psychiatry · doi:10.1016/s2215-0366(18)30230-x · Cited by 40
Usability and acceptability assessment of an empathic virtual agent to prevent major depression
Bresó, A., Martínez‐Miranda, J., Botella, C., Baños, R. M., & García‐Gómez, J. M. (2016) · Expert Systems · doi:10.1111/exsy.12151 · Cited by 36
Wallin, E., Norlund, F., Olsson, E. M. G., Burell, G., Held, C., & Carlsson, T. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9690 · Cited by 36 · cited 1×
“Asynchronous support may reduce issues with scheduling, but research suggests few patients involved in a DMHI with an option to send messages to a therapist and receive asynchronous feedback took advantage of this feature; most patients sent fewer than two messages over the course of 14 weeks [47].”
BackgroundWörtwein, T., & Scherer, S. J. (2017) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2017.8273573 · Cited by 33
Detection of Dementia from Responses to Atypical Questions Asked by Embodied Conversational Agents
Ujiro, T., Tanaka, H., Adachi, H., Kazui, H., Ikeda, M., Kudo, T., & Nakamura, S. (2018) · Interspeech (USB) · doi:10.21437/interspeech.2018-1514 · Cited by 32
Wargnier, P., Benveniste, S., Jouvelot, P., & Rigaud, A.-S. (2018) · Technology and Disability · doi:10.3233/tad-180189 · Cited by 32 · cited 1×
“various groups including children, adolescents, adults, elders [66], and specific clinical populations [67].”
BackgroundBots Acting Like Humans: Understanding and Preventing Harm
Daniel, F., Cappiello, C., & Benatallah, B. (2019) · IEEE Internet Computing · doi:10.1109/mic.2019.2893137 · Cited by 28
The Role of Agent Technologies in Personalized Medicine
Ivanović, M. D., & Semnic, M. D. (2018) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2018.8599421 · Cited by 22
Virtual Assistant to Suicide Prevention in Individuals with Borderline Personality Disorder
Wibhowo, C., & Sanjaya, R. (2021) · International Conference on Computer and Information Sciences · doi:10.1109/iccoins49721.2021.9497160 · Cited by 17
An analysis of language models for metaphor recognition
Neidlein, A., Wiesenbach, P., & Markert, K. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.332 · Cited by 16
Trusting the bot: Addressing the ethical challenges of consumer digital mental health therapy
Martinez‐Martin, N. (2020) · Developments in neuroethics and bioethics · doi:10.1016/bs.dnb.2020.03.003 · Cited by 14
Filippis, M. L. D., Federici, S., Mele, M. L., Borsci, S., Bracalenti, M., Gaudino, G., Cocco, A., Amendola, M., & Simonetti, E. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58796-3_30 · Cited by 13
Carpenter, J. M., Crutchley, P., Zilca, R., Schwartz, H. A., Smith, L. K., Cobb, A. M., & Parks, A. C. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.8099 · Cited by 13
Self-citationAn Empathy Imitation Game: Empathy Turing Test for Care- and Chat-bots
Howick, J., Morley, J., & Floridi, L. (2021) · Minds and Machines · doi:10.1007/s11023-021-09555-w · Cited by 11
Alattas, A., Teepe, G. W., Leidenberger, K., Fleisch, E., Car, L. T., Salamanca-Sanabria, A., & Kowatsch, T. (2021) · International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC) · doi:10.5220/0010413308010808 · Cited by 9
CONTEXTUAL DIFFERENCES IN THE DYNAMIC MEASUREMENT OF TRUST IN WEB SITES ACROSS DOMAINS
Asan, O. (2012) · International Journal of Cyber Society and Education · doi:10.7903/ijcse.969 · Cited by 4
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
Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review (Preprint)
Laymouna, M., Ma, Y., Lessard, D., Schuster, T., Engler, K., & Lebouché, B. (2024) · doi:10.2196/preprints.56930 · Cited by 302
Roles, Users, Benefits, and Limitations of Chatbots in Health Care: Rapid Review
Laymouna, M., Ma, Y., Lessard, D., Schuster, T., Engler, K., & Lebouché, B. (2024) · Journal of Medical Internet Research · doi:10.2196/56930 · Cited by 305
Artificial intelligence technologies and compassion in healthcare: A systematic scoping review
Morrow, E. M., Zidaru, T., Ross, F., Mason, C. L., Patel, K. D., Ream, M., & Stockley, R. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.971044 · Cited by 308
To chat or bot to chat: Ethical issues with using chatbots in mental health
Coghlan, S., Leins, K., Sheldrick, S., Cheong, M., Gooding, P., & D'Alfonso, S. (2023) · DIGITAL HEALTH · doi:10.1177/20552076231183542 · Cited by 215
Khawaja, Z., & Bélisle-Pipon, J.-C. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1278186 · Cited by 214
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
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
