Evidence map›Paper›PMID 33063526›Full record

SynthesisCanadian journal of psychiatry. Revue canadienne de psychiatrie2021

Changes to the Psychiatric Chatbot Landscape: A Systematic Review of Conversational Agents in Serious Mental Illness: Changements du paysage psychiatrique des chatbots: une revue systématique des agents conversationnels dans la maladie mentale sérieuse.

Aditya Nrusimha Vaidyam, Danny Linggonegoro, John Torous

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Canadian journal of psychiatry. Revue canadienne de psychiatrie, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  14. Allies not enemies-creating a more empathetic and uplifting patient experience through technology and art.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Aditya Nrusimha Vaidyam1859Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-2900-4561
Danny Linggonegoro1859Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
John Torous1859Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.ORCID 0000-0002-5362-7937

Funding

Digital Phenotyping for Computational Models of Relapse Prediction in Early Course PsychosisK23MH116130 · NIMH · BETH ISRAEL DEACONESS MEDICAL CENTER · PI TOROUS, JOHN · 2018 to 2021
$872k
NIMH NIH HHS K23 MH116130
6 · The paper itself

Abstract

objectiveThe need for digital tools in mental health is clear, with insufficient access to mental health services. Conversational agents, also known as chatbots or voice assistants, are digital tools capable of holding natural language conversations. Since our last review in 2018, many new conversational agents and research have emerged, and we aimed to reassess the conversational agent landscape in this updated systematic review.

methodsA systematic literature search was conducted in January 2020 using the PubMed, Embase, PsychINFO, and Cochrane databases. Studies included were those that involved a conversational agent assessing serious mental illness: major depressive disorder, schizophrenia spectrum disorders, bipolar disorder, or anxiety disorder.

resultsOf the 247 references identified from selected databases, 7 studies met inclusion criteria. Overall, there were generally positive experiences with conversational agents in regard to diagnostic quality, therapeutic efficacy, or acceptability. There continues to be, however, a lack of standard measures that allow ease of comparison of studies in this space. There were several populations that lacked representation such as the pediatric population and those with schizophrenia or bipolar disorder. While comparing 2018 to 2020 research offers useful insight into changes and growth, the high degree of heterogeneity between all studies in this space makes direct comparison challenging.

conclusionsThis review revealed few but generally positive outcomes regarding conversational agents' diagnostic quality, therapeutic efficacy, and acceptability, which may augment mental health care. Despite this increase in research activity, there continues to be a lack of standard measures for evaluating conversational agents as well as several neglected populations. We recommend that the standardization of conversational agent studies should include patient adherence and engagement, therapeutic efficacy, and clinician perspectives.

Indexed as

Major Depressive DisorderMental Health ServicesChildCommunicationHumansLanguageMental Healthchatbotconversational agentmental healthpsychiatryserious mental illness

Identifiers

PMID33063526
PMCPMC8172347

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.