The DOI matches its Crossref record; 49 of the 54 references it cites were found in a registry or an index, and 5 are not checked yet; none of the 54 sources its claims cite is checked for support yet.
An overview of the features of chatbots in mental health: A scoping review
Open accessAbstract
BACKGROUND Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual languages. Chatbots have the potential to be useful tools for individuals with mental disorders, especially those who are reluctant to seek mental health advice due to stigmatization. While numerous studies have been conducted about using chatbots for mental health, there is a need to systematically bring this evidence together in order to inform mental health providers and potential users about the main features of chatbots and their potential uses, and to inform future research about the main gaps of the previous literature. OBJECTIVE We aimed to provide an overview of the features of chatbots used by individuals for their mental health as reported in the empirical literature. METHODS Seven bibliographic databases (Medline, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar) were used in our search. In addition, backward and forward reference list checking of the included studies and relevant reviews was conducted. Study selection and data extraction were carried out by two reviewers independently. Extracted data were synthesised using a narrative approach. Chatbots were classified according to their purposes, platforms, response generation, dialogue initiative, input and output modalities, embodiment, and targeted disorders. RESULTS Of 1039 citations retrieved, 53 unique studies were included in this review. The included studies assessed 41 different chatbots. Common uses of chatbots were: therapy (n = 17), training (n = 12), and screening (n = 10). Chatbots in most studies were rule-based (n = 49) and implemented in stand-alone software (n = 37). In 46 studies, chatbots controlled and led the conversations. While the most frequently used input modality was written language only (n = 26), the most frequently used output modality was a combination of written, spoken and visual languages (n = 28). In the majority of studies, chatbots included virtual representations (n = 44). The most common focus of chatbots was depression (n = 16) or autism (n = 10). CONCLUSION Research regarding chatbots in mental health is nascent. There are numerous chatbots that are used for various mental disorders and purposes. Healthcare providers should compare chatbots found in this review to help guide potential users to the most appropriate chatbot to support their mental health needs. More reviews are needed to summarise the evidence regarding the effectiveness and acceptability of chatbots in mental health.
Checked 2026-10-06 · 5 references not checked yet
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This scoping review identified 53 studies assessing 41 different chatbots used by individuals for mental health, most commonly for therapy, training, and screening. Chatbots were predominantly rule-based, stand-alone, chatbot-led, embodied, and focused on depression or autism, with the authors concluding that research is nascent and needs more RCTs and standardized outcome/reporting measures.
Key findings
- Of 1039 citations retrieved, 53 unique studies were included, assessing 41 different chatbots. Common uses were therapy (n=17), training (n=12), and screening (n=10); other uses were self-management (n=7), counselling (n=5), education (n=4), and diagnosis (n=2).
- Most chatbots were rule-based (n=49), implemented in stand-alone software (n=37), controlled/led the conversation (n=46), and included virtual representations (n=44). The most common input modality was written language only (n=26), while the most common output modality was a combination of written, spoken, and visual languages (n=28).
- Chatbots most often targeted depression (n=16) or autism (n=10), followed by posttraumatic stress disorder (n=7), anxiety (n=7), substance use disorders (n=5), schizophrenia (n=3), dementia (n=3), stress (n=2), phobia (n=2), eating disorders (n=1), and any mental disorder (n=7).
- Included studies were mostly quasi-experimental (n=23, 43%) and journal articles (n=35, 66%); 23 studies (43%) were from the USA, and 30 (56.6%) were published between 2016 and 2019. Mean sample size was 75.2 (range 4–454), mean age was 37.1 years, and mean male percentage was 55%; acceptability (n=36) and effectiveness (n=33) were the most commonly assessed outcomes.
Methods
- Scoping review following PRISMA-ScR. Searches covered MEDLINE, EMBASE, PsycINFO, Scopus, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar (first 100 citations), conducted 10–12 May 2019, plus backward and forward reference list checking.
- Inclusion required primary research on chatbots used by individuals for mental health, on stand-alone software or web browser platforms, excluding robotics, serious games, SMS, telephones, physician/caregiver-specific chatbots, and human-operated dialogue. Peer-reviewed articles, dissertations, conference proceedings, and reports in English were eligible; no restrictions were placed on dialogue initiative, input/output modality, study design, setting, outcome, year, or country.
- Two reviewers independently selected studies and extracted data, resolving disagreements with a third reviewer. Cohen’s kappa was 0.80 and 0.84 for the two selection steps and 0.74 for data extraction. Data were synthesized narratively, with chatbots classified by purpose, platform, response generation, dialogue initiative, input/output modalities, embodiment, and targeted disorders. No quality assessment was performed.
Limitations
- The paper states that restricting platforms to stand-alone software and web browser (excluding robotics, serious games, SMS, and telephones) and dialogue initiative to user/system (not human operator) excluded more than 25 studies. It also states that practical constraints prevented searching Web of Science and ProQuest, manual searching, and contacting experts, so some studies were likely missed; excluding non-primary papers may also miss chatbots reported in proposals, book chapters, and conference abstracts.
- The paper states that it did not assess study quality because scoping reviews usually do not, and it did not summarize included studies’ results because of the review’s aim and the large number of studies assessing different outcomes.
- Evident in the text: included studies were inconsistent in outcome measurement and in reporting chatbot characteristics, study methods, and sample characteristics; sample size was <50 in 30 studies, many studies recruited non-clinical samples, and less than half of effectiveness studies (14 of 33) used randomized controlled trials, limiting comparability and generalisability.
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Claims the paper makes
76 Own result · 31 Not checked yet · 13 No basis given · 9 Own method · 5 Own interpretation
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A clinical sample relating to a specific mental disorder featured in about 58.5% of the studies (n=31).”
hedgedn = 31- Asserted: passes — The authors put it forward as true.
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In the paper
“In total, 53 publications were included in the synthesis.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The included studies assessed 41 different chatbots.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The most common outcomes assessed by the included studies were acceptability (n=36) and effectiveness of the chatbots (n=33).”
plainn = 36- Asserted: passes — The authors put it forward as true.
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“the most commonly used study design was quasi-experimental (n=23, 43%).”
plainn = 23- Asserted: passes — The authors put it forward as true.
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In the paper
“counselling (n=5)”
plainn = 5- Asserted: passes — The authors put it forward as true.
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“Chatbots were implemented in stand-alone software in 70% of studies”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“only written language via keyboards and mouse (n=26)”
plainn = 26- Asserted: passes — The authors put it forward as true.
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“autism (n=10)”
plainn = 10- Asserted: passes — The authors put it forward as true.
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“both chatbots and users could lead the dialogue in the remaining 7 studies.”
hedgedn = 7- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 36
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- The quote only partly states this claim. 1
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In the paper
“the search process of six bibliographic databases retrieved 1039 citations.”
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“The mean sample size was 75.2, it ranged from 4 to 454.”
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“whereas it was 200 or more in only 4 studies.”
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“After scanning their titles and abstracts, 505 citations were excluded.”
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“By reading the full text of the 125 remaining citations, 43 publications were included.”
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“The sample size was less than 50 in 30 studies”
plainn = 30- Asserted: passes — The authors put it forward as true.
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“After removing 409 duplicates, 630 unique titles and abstracts remained.”
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“Sex of participants was reported in 41 studies, the mean of male percentage in all those studies was 55%.”
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In the paper
“More than half of the studies were published between 2016 and 2019 (n=30, 56.6%).”
plainn = 30- Asserted: passes — The authors put it forward as true.
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“Age of participants was reported in 33 studies, the mean age of participants in all those studies was 37.1 years.”
plainn = 33- Asserted: passes — The authors put it forward as true.
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“about 43% of studies were published in the USA (n=23).”
plainn = 23- Asserted: passes — The authors put it forward as true.
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“Participants were recruited from either clinical settings (n=20), community (n=16), or educational settings (n=20).”
plainn = 20- Asserted: passes — The authors put it forward as true.
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“Six additional studies were identified from backward reference list checking, and four studies were identified from forward reference list checking.”
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“About two thirds of studies were journal articles (n=35, 66%).”
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“The chatbots were given names in 39 studies”
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“The majority of chatbots (92.5%) depended on only decision trees to generate their responses; only 7.5% used machine learning approaches.”
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“a combination of spoken and visual languages (n=11)”
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“while one study used supervised machine learning, another study used reinforcement learning. However, the remaining two studies did not specify the machine learning approaches used.”
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“only written language (n=9)”
plainn = 9- Asserted: passes — The authors put it forward as true.
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“a combination of written and visual languages (2).”
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“a combination of written and visual languages (n=5)”
plainn = 5- Asserted: passes — The authors put it forward as true.
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“a combination of written and spoken languages (n=7)”
plainn = 7- Asserted: passes — The authors put it forward as true.
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In the paper
“We identified 53 studies that assessed 41 different chatbots.”
- Also stated in: § 5 Conclusion · plain
“We identified 41 different chatbots in mental health.”
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- Also stated in: § 5 Conclusion · plain
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In the paper
“For example, it was challenging for the reviewers to identify chatbots' platforms, their input and output modality, type of response generation, and dialogue initiative.”
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This scoping review followed guidelines recommended by the PRISMA Extension for Scoping Reviews (PRISMA-ScR) [23].”
What the sources say
- Not checked yetcites [23] [23] A.C. Tricco, E. Lillie, W. Zarin, K.K. O'Brien, H. Colquhoun, D. Levac, D. Moher, M.D. Peters, T. Horsley, L. Weeks, PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation, Annals of internal medicine, 169 (2018) 467-473. source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“we developed a data extraction form and piloted it using five included studies (Appendix B).”
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Inter-coder agreement between both reviewers w as assessed using Cohen's kappa [24], which was 0.80 and 0.84 in the first and second step of the selection process, respectively, indicating a very good agreement [25].”
What the sources say
- Not checked yetcites [24] [24] J. Higgins, J. Deeks, Chapter 7: selecting studies and collecting data, in: J. Higgins, S. Green (Eds.) Cochrane Handbook for Systematic Reviews of Interventions, John Wiley & Sons, Sussex, UK, 2008, pp. 151-185.
- Not checked yetcites [25] [25] D.G. Altman, Practical statistics for medical research, CRC press1990.
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“That review used limited search terms and therefore identified a relatively low number of studies (i.e. 10) [6].”
What the sources say
- Not checked yetcites [6] [6] A.N. Vaidyam, H. Wisniewski, J.D. Halamka, M.S. Kashavan, J.B. Torous, Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape, The Canadian Journal of Psychiatry, (2019) 0706743719828977. source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“According to the World Health Organization [9], 29% of the 15,000 mobile health apps focused on mental health.”
What the sources say
- Not checked yetcites [9] [9] E. Anthes, Mental health: there's an app for that, Nature News, 532 (2016) 20. source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Inter-coder agreement between the reviewers was good ( Cohen's kappa = 0.74) [25].”
What the sources say
- Not checked yetcites [25] [25] D.G. Altman, Practical statistics for medical research, CRC press1990.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“There is a global shortage of mental health workers, with demand out-stripping service provision [6].”
What the sources say
- Not checked yetcites [6] [6] A.N. Vaidyam, H. Wisniewski, J.D. Halamka, M.S. Kashavan, J.B. Torous, Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape, The Canadian Journal of Psychiatry, (2019) 0706743719828977. source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“mental health services do not reach about 55% and 85% of people in developed and developing countries, respectively [9].”
What the sources say
- Not checked yetcites [9] [9] E. Anthes, Mental health: there's an app for that, Nature News, 532 (2016) 20. source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The lack of access to mental health services may lead to suicidal behaviour, resulting in increasing mortality [10].”
What the sources say
- Not checked yetcites [10] [10] R.D. Hester, Lack of access to mental health services contributing to the high suicide rates among veterans, International journal of mental health systems, 11 (2017) 47. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This was surprising because web-based chatbots are considered more appropriate than stand stand-alone software for the following two reasons.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“In comparison with artificial intelligence chatbots, rule-based chatbots are more simple to develop and less prone to errors as their responses are predetermined and they do not need to create new responses [41].”
What the sources say
- Not checked yetcites [41] [41] K. Shridhar, Rule based bots vs AI bots, Medium.com, 2017.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Further, rule-based chatbots are considered more secure and responsible than artificial intelligence chatbots [42].”
What the sources say
- Not checked yetcites [42] [42] Hubtype, Rule-Based vs AI Chatbots, 2018.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“This may be attributed to the fact that depression and anxiety are the most common mental disorder around the world [44, 45], and chatbots are reported to be effective in improving social skills for patients with autism [14, 17, 46-48].”
What the sources say
- Not checked yetcites [44] [44] A.J. Ferrari, F.J. Charlson, R.E. Norman, S.B. Patten, G. Freedman, C.J. Murray, T. Vos, H.A. Whiteford, Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010, PLoS medicine, 10 (2013) e1001547. source
- Not checked yetcites [45] [45] H. Ritchie, M. Roser, Mental Health, Our World in Data, 2018.
- Not checked yetcites [14] [14] M.R. Ali, Z. Rasazi, A.A. Mamun, R. Langevin, R. Rawassizadeh, L. Schubert, M.E. Hoque, A Virtual Conversational Agent for Teens with Autism: Experimental Results and Design Lessons, arXiv preprint arXiv:1811.03046, (2018).
- Not checked yetcites [17] [17] S.Z. Razavi, M.R. Ali, T.H. Smith, L.K. Schubert, M.E. Hoque, The LISSA virtual human and ASD teens: An overview of initial experiments, International Conference on Intelligent Virtual Agents, Springer, 2016, pp. 460-463.
- Not checked yetcites [46] [46] M. Milne, M.H. Luerssen, T.W. Lewis, R.E. Leibbrandt, D.M.W. Powers, Development of a virtual agent based social tutor for children with autism spectrum disorders, The 2010 International Joint Conference on Neural Networks (IJCNN), 2010, pp. 1-9.
- Not checked yetcites [47] [47] H. Tanaka, H. Negoro, H. Iwasaka, S. Nakamura, Embodied conversational agents for multimodal automated social skills training in people with autism spectrum disorders, PLoS ONE Vol 12(8), 2017, ArtID e0182151, 12 (2017).
- Not checked yetcites [48] [48] H. Tanaka, S. Sakti, G. Neubig, T. Toda, H. Negoro, H. Iwasaka, S. Nakamura, Automated Social Skills Trainer, Proceedings of the 20th International Conference on Intelligent User Interfaces - IUI '15, 2015, pp. 17-27.
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Firstly, to use web-based chatbots, users do not need to install a specific application to their devices, thereby reducing the risk of breaching their privacy.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“The rareness of chatbots led by users was also found in another review [29].”
What the sources say
- Not checked yetcites [29] [29] L. Laranjo, A.G. Dunn, H.L. Tong, A.B. Kocaballi, J. Chen, R. Bashir, D. Surian, B. Gallego, F. Magrabi, A.Y. Lau, Conversational agents in healthcare: a systematic review, Journal of the American Medical Informatics Association, 25 (2018) 1248-1258. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This was expected since most chatbots used rule-based approaches, which restrict user input to predefined words and replies, thereby, prevent them to lead or control the dialogue [28].”
What the sources say
- Not checked yetcites [28] [28] M.F. McTear, Spoken dialogue technology: enabling the conversational user interface, ACM Computing Surveys (CSUR), 34 (2002) 90-169. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This made our review more comprehensive than the two previous reviews [6, 22]; where the former focused on some mental disorders and the latter focused on only embodied chatbots.”
What the sources say
- Not checked yetcites [6] [6] A.N. Vaidyam, H. Wisniewski, J.D. Halamka, M.S. Kashavan, J.B. Torous, Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape, The Canadian Journal of Psychiatry, (2019) 0706743719828977. source
- Not checked yetcites [22] [22] S. Provoost, H.M. Lau, J. Ruwaard, H. Riper, Embodied conversational agents in clinical psychology: a scoping review, Journal of Medical Internet Research, 19 (2017) e151. source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The study selection process and data extraction process were conducted by two reviewers independently to reduce selection bias.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This enabled us to minimise the risk of publication bias.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“We excluded systems that are controlled by human operator as they are more similar to telemedicine systems than chatbots in terms of system design.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“The above-mentioned restriction was also applied in the previous two reviews [6, 22].”
What the sources say
- Not checked yetcites [6] [6] A.N. Vaidyam, H. Wisniewski, J.D. Halamka, M.S. Kashavan, J.B. Torous, Chatbots and Conversational Agents in Mental Health: A Review of the Psychiatric Landscape, The Canadian Journal of Psychiatry, (2019) 0706743719828977. source
- Not checked yetcites [22] [22] S. Provoost, H.M. Lau, J. Ruwaard, H. Riper, Embodied conversational agents in clinical psychology: a scoping review, Journal of Medical Internet Research, 19 (2017) e151. source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Agreement between reviewers was very good for the study selection process and good for the data extraction process.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Research regarding chatbots in mental health is emerging, and this was apparent from publication year of studies (most studies were published in the last 9 years), lack of randomized controlled trials, and the inconsistency of the studies in terms of outcome measures and reporting of characteristics of chatbots and study design.”
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Therefore, we adapted several taxonomies [28-30] to characterise the chatbots in our review.”
What the sources say
- Not checked yetcites [28] [28] M.F. McTear, Spoken dialogue technology: enabling the conversational user interface, ACM Computing Surveys (CSUR), 34 (2002) 90-169. source
- Not checked yetcites [29] [29] L. Laranjo, A.G. Dunn, H.L. Tong, A.B. Kocaballi, J. Chen, R. Bashir, D. Surian, B. Gallego, F. Magrabi, A.Y. Lau, Conversational agents in healthcare: a systematic review, Journal of the American Medical Informatics Association, 25 (2018) 1248-1258. source
- Not checked yetcites [30] [30] J. Chu-Carroll, M.K. Brown, Tracking initiative in collaborative dialogue interactions, Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, Association for Computational Linguistics, 1997, pp. 262-270.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Agreement between reviewers was very good for the study selection process and good for the data extraction process.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Owing to practical constraints, we could not search other databases that cover both fields (e.g. Web of Science and ProQuest), conduct manual search, and contact experts.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Although chatbots were included in this review regardless of their purpose, type of response generation, input and output modalities, embodiment, and targeted disorders, we restricted their platforms to stand-alone software and web browser (but not robotics, serious games, SMS, nor telephones) and restricted their type of dialogue initiative to users and system (but not human operator).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [17]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI-augmented interventions could help address this problem, while maintaining a low level of resource utilization (no or reduced therapist involvement) through the use of human-like communication, intelligent treatment reminders, environment sensing to deliver more accurate treatment engagement support, and symptom monitoring (17, 20, 22, 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 [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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 [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, most of these interventions relied on retrieval-based chatbots, which use predefined responses or static databases and often result in rigid, repetitive interactions [17-19].”
From Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues (2025) · cites this paper as [18]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, most of these interventions relied on retrieval-based chatbots, which use predefined responses or static databases and often result in rigid, repetitive interactions [17-19].”
From Generative AI Mental Health Chatbots as Therapeutic Tools: Systematic Review and Meta-Analysis of Their Role in Reducing Mental Health Issues (2025) · cites this paper as [18]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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 [17]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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 [17]
Experimental — accuracy not yet measuredIn the citing paper
“In contrast to rule-based chatbots, artificial intelligence chatbots can generate responses to complicated queries and enable users to control the conversation [13].”
From Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis (2020) · cites this paper as [13]
In the citing paper
“According to a scoping review conducted by Abd-alrazaq et al [13], chatbots are used for many mental disorders, such as autism, post-traumatic stress disorder, substance use disorders, schizophrenia, and dementia.”
From Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis (2020) · cites this paper as [13]
In the citing paper
“For this reason, 3 previous reviews about chatbots applied these restrictions [5,13,19].”
From Effectiveness and safety of using chatbots to improve mental health: systematic review and meta-analysis (2020) · cites this paper as [13]
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 [40]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 3.2 Description of included studies
“Participants were recruited from either clinical settings (n=20), community (n=16), or educational settings (n=20).”
§ 3.2 Description of included studies
“sample:31 Clinical:21”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 3.2 Description of included studies
“More than half of the studies were published between 2016 and 2019 (n=30, 56.6%).”
§ 3.2 Description of included studies
“2016-2019:31”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Abstract
“Seven bibliographic databases (Medline, Embase, PsycINFO, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar) were used in our search.”
§ 2.1.1 Search sources
“we searched the following bibliographic databases: MEDLINE, EMBASE, PsycINFO, Scopus, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 3.1 Search results
“the search process of six bibliographic databases retrieved 1039 citations.”
§ 2.1.1 Search sources
“we searched the following bibliographic databases: MEDLINE, EMBASE, PsycINFO, Scopus, Cochrane Central Register of Controlled Trials, IEEE Xplore, ACM Digital Library, and Google Scholar.”
Citations
Cites
PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0850 · Cited by 43,565
Scoping studies: towards a methodological framework
Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742
Cochrane Handbook for Systematic Reviews of Interventions: Cochrane Book Series
(2008) · doi:10.1002/9780470712184 · Cited by 25,048
von Elm, E., Altman, D. G., Egger, M., Pocock, S. J., Gøtzsche, P. C., Vandenbroucke, J. P., & Initiative, F. T. S. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-147-8-200710160-00010 · Cited by 17,796
Practical Statistics for Medical Research.
Walter, S., & Altman, D. G. (1992) · Biometrics · doi:10.2307/2532320 · Cited by 11,666
A typology of reviews: an analysis of 14 review types and associated methodologies
Grant, M. J., & Booth, A. (2009) · Health Information & Libraries Journal · doi:10.1111/j.1471-1842.2009.00848.x · Cited by 10,817
Murray, C. J. L., Vos, T., Lozano, R., Naghavi, M., Flaxman, A. D., Michaud, C., Ezzati, M., Shibuya, K., Salomon, J. A., Abdalla, S., et al. (2012) · The Lancet · doi:10.1016/s0140-6736(12)61689-4 · Cited by 8,888
Ferrari, A. J., Charlson, F. J., Norman, R. E., Patten, S. B., Freedman, G., Murray, C. J. L., Vos, T., & Whiteford, H. A. (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001547 · Cited by 3,115
The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013
Steel, Z., Marnane, C., Iranpour, C., Chey, T., Jackson, J. W., Patel, V., & Silove, D. (2014) · International Journal of Epidemiology · doi:10.1093/ije/dyu038 · Cited by 2,846
Fitzpatrick, K. K., Darcy, A., & Vierhile, M. (2017) · JMIR Mental Health · doi:10.2196/mental.7785 · Cited by 2,730 · cited 1×
“For example, the chatbots “Woebot” and “Help4Mood” were developed to deliver cognitive behavioural therapy for patients with depression and anxiety [31, 32].”
Background
Research works printed but not linked: 2
- Luerssen, 2018, AustraliaVirtual Agents as a Service: Applications in Healthcare, Proceedings of the 18th International Conference on Intelligent Virtual Agents, ACM, Sydney, NSW2018, Virtual Agents as a Service: Applications in Healthcare, Proceedings of the 18th International Conference on Intelligent Virtual Agents, ACM, Sydney, NSW
- Higgins, 2008, Chapter 7: selecting studies and collecting data, 151
Cited by
Sorted by citations
Items 601–607 of 607
Saripudin, M., Badrujaman, A., Karsih, Herdi, Justitia, D., & Hikmy, B. J. (2026) · International Conference on Computer, Control, Informatics and its Applications · doi:10.1109/ic3ina72033.2026.11714696
Цифровая трансформация психологического сопровождения личности в тензионных состояниях
Фрейманис, И. Ф., & Игнатова, Е. С. (2026) · Психиатрия психотерапия и клиническая психология · doi:10.34883/pi.2026.17.1.027
Cheng, N., Yu, R., & Wang, X. (2026) · Ergonomics · doi:10.1080/00140139.2026.2748363
Can AI Assistants Trigger Mental Illness in Users?
Diamandis, E. P. (2026) · F1000Research · doi:10.12688/f1000research.191633.1
Alkhamees, A. A. (2026) · Diagnostics · doi:10.3390/diagnostics16213560
Coulombe, S., Rickard, N. S., Pacheco, T., Francis, J., Vella-Brodrick, D., Dionne, É., Shestopaloff, A., Vokey, F., Hoang, N. B. L., Leitch, J., et al. (2026) · doi:10.2196/preprints.114548
Dinet, J., Verdel, H., & Schneider, B. (2026) · Pratiques Psychologiques · doi:10.1016/j.prps.2026.09.002
Metadata
- Authors
- Abd-alrazaq, A. A., Alajlani, M., Alalwan, A. A., Bewick, B. M., Gardner, P., & Househ, M.
- Affiliations
- Hamad bin Khalifa University (QA)
- University of Warwick (GB)
- Al-Balqa Applied University (JO)
- University of Leeds (GB)
- Corresponding author
- Househ, Mowafa
- ORCID
- Venue
- International Journal of Medical Informatics
- Publisher
- Elsevier BV
- Published
- 2019-12
- Volume
- 132
- Pages
- 103978
- ISSN
- 1386-5056 (ISSN-L)
- OpenAlex
- W2977128309
- Semantic Scholar
- ee757d582438057b79e60bf410bc459279398d73
- Cited by
- 605
- References
- 53
- Citations by year
- 2026: 122 · 2025: 117 · 2024: 112 · 2023: 108 · 2022: 65 · 2021: 55 · 2020: 24 · 2019: 1
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- green
- wrap.warwick.ac.uk
- Licence
- Locations
- International Journal of Medical Informatics (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Warwick Research Archive Portal (University of Warwick) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- chatbots, mental health, depression, autism spectrum disorder, rule-based chatbots, virtual agents, chatbot therapy, mental health screening, mental health training, stigma, embodiment, dialogue control, chatbot response generation, mental illness, chatbot evaluation metrics
- MeSH terms
- Behavior Therapy, Mental Disorders, Communication, Humans, Mental Health, Telemedicine
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.
A language model drew the claims from the paper on 2026-10-06, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-06 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
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