Evidence map›Paper›PMID 39957757›Full record

ArticleFrontiers in psychiatry2024

Artificial intelligence conversational agents in mental health: Patients see potential, but prefer humans in the loop.

Hyein S Lee, Colton Wright, Julia Ferranto, Jessica Buttimer, Clare E Palmer, Andrew Welchman, Kathleen M Mazor, Kimberly A Fisher, David Smelson, Laurel O'Connor and 2 more

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 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

27 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

12 authors.

Hyein S LeeProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Colton WrightProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Julia FerrantoProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Jessica ButtimerIeso Digital Health, Cambridge, United Kingdom.
Clare E PalmerIeso Digital Health, Cambridge, United Kingdom.
Andrew WelchmanIeso Digital Health, Cambridge, United Kingdom.
Kathleen M MazorDivision of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Kimberly A FisherDivision of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
David SmelsonDivision of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Laurel O'ConnorProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Nisha FaheyProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.
Apurv SoniProgram in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Digital mental health interventions, such as artificial intelligence (AI) conversational agents, hold promise for improving access to care by innovating therapy and supporting delivery. However, little research exists on patient perspectives regarding AI conversational agents, which is crucial for their successful implementation. This study aimed to fill the gap by exploring patients' perceptions and acceptability of AI conversational agents in mental healthcare. Methods: Adults with self-reported mild to moderate anxiety were recruited from the UMass Memorial Health system. Participants engaged in semi-structured interviews to discuss their experiences, perceptions, and acceptability of AI conversational agents in mental healthcare. Anxiety levels were assessed using the Generalized Anxiety Disorder scale. Data were collected from December 2022 to February 2023, and three researchers conducted rapid qualitative analysis to identify and synthesize themes. Results: The sample included 29 adults (ages 19-66), predominantly under age 35, non-Hispanic, White, and female. Participants reported a range of positive and negative experiences with AI conversational agents. Most held positive attitudes towards AI conversational agents, appreciating their utility and potential to increase access to care, yet some also expressed cautious optimism. About half endorsed negative opinions, citing AI's lack of empathy, technical limitations in addressing complex mental health situations, and data privacy concerns. Most participants desired some human involvement in AI-driven therapy and expressed concern about the risk of AI conversational agents being seen as replacements for therapy. A subgroup preferred AI conversational agents for administrative tasks rather than care provision. Conclusions: AI conversational agents were perceived as useful and beneficial for increasing access to care, but concerns about AI's empathy, capabilities, safety, and human involvement in mental healthcare were prevalent. Future implementation and integration of AI conversational agents should consider patient perspectives to enhance their acceptability and effectiveness.

Indexed as

anxietyartificial intelligencechatbotscognitive behavioral therapyconversational agentsmental healthpatient perspectivesqualitative

Identifiers

PMID39957757
PMCPMC11826059

What OpenQuestion holds

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LicenceCC BY
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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.