Evidence map›Paper›PMID 42536834›Full record

ArticleJournal of participatory medicine2026

Perspectives of Individuals With Obsessive-Compulsive Disorder on the Role of Artificial Intelligence in Therapy and Treatment: Thematic Qualitative Study.

Daniel Mokhtar, Harrison Wang, Emma C Garland, Dejan Shakya, Lucas Occhino-Moede, Xiao Liu, Adam Charles Frank

Abstract read
In one paragraph

Article in Journal of participatory medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Daniel MokhtarDepartment of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, 2500 Alcazar St, Suite 2200, Los Angeles, CA, 90033, United States, 1 323-442-6000, 1 323-442-6001.ORCID http://orcid.org/0009-0008-0011-5963
Harrison WangDepartment of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, 2500 Alcazar St, Suite 2200, Los Angeles, CA, 90033, United States, 1 323-442-6000, 1 323-442-6001.ORCID http://orcid.org/0000-0002-9941-6895
Emma C GarlandDepartment of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, 2500 Alcazar St, Suite 2200, Los Angeles, CA, 90033, United States, 1 323-442-6000, 1 323-442-6001.ORCID http://orcid.org/0009-0007-6799-925X
Dejan ShakyaDepartment of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, 2500 Alcazar St, Suite 2200, Los Angeles, CA, 90033, United States, 1 323-442-6000, 1 323-442-6001.ORCID http://orcid.org/0000-0003-0281-3534
Lucas Occhino-MoedeDepartment of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, 2500 Alcazar St, Suite 2200, Los Angeles, CA, 90033, United States, 1 323-442-6000, 1 323-442-6001.ORCID http://orcid.org/0009-0002-8450-5534
Xiao LiuDepartment of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, 2500 Alcazar St, Suite 2200, Los Angeles, CA, 90033, United States, 1 323-442-6000, 1 323-442-6001.ORCID http://orcid.org/0009-0001-9406-5301
Adam Charles FrankDepartment of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, 2500 Alcazar St, Suite 2200, Los Angeles, CA, 90033, United States, 1 323-442-6000, 1 323-442-6001.ORCID http://orcid.org/0000-0001-8203-8480

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: AI has become increasingly used in mental health care for applications such as diagnosis, monitoring, and treatment support. These include tools like clinician support systems, large language models, and conversational agents used to augment psychotherapy and clinical decision-making. While prior research suggests potential benefits of and concerns with AI, little is known within the domain of obsessive-compulsive disorder (OCD). Given the expanding role of AI in psychiatry, understanding these perspectives is essential to ensuring AI implementation aligns with patient priorities and values. Objective: This study aims to explore the perspectives of individuals with OCD on the use of AI in health care, including perceived benefits, risks, and its role in relation to human clinicians. Methods: We conducted semistructured interviews with 24 adults self-reporting OCD, recruited through online communities and advocacy networks. Eligible individuals (≥18 y with self-reported OCD) completed screening, provided informed consent, and participated in remote Health Insurance Portability and Accountability Act (HIPAA)-compliant Zoom (Zoom Communications, Inc) interviews (May-December 2024). Transcripts were deidentified, open-coded, and used to develop a codebook. Focused codes were applied using a thematic analysis framework in Dedoose (v9.2.22; Sociocultural Research Consultants, LLC). Each transcript was independently coded by 2 reviewers, with discrepancies resolved through consensus. Themes were developed through iterative interpretive analysis of code clusters. Results: Participants' perspectives encompassed concerns and benefits of AI in mental health care. Participants expressed concerns about the accuracy and efficacy of information provided by AI, as well as a limited ability for clinical judgment in psychiatric care. Additionally, participants emphasized the importance of human connection, particularly therapeutic alliance, empathy, and reassurance provided by clinicians, which they felt AI could not replicate. Concerns about data privacy, security, and downstream use of information were also highlighted. Despite concerns, many endorsed the use of AI as an adjunct rather than a replacement for clinicians, noting potential benefits in symptom monitoring, preliminary information gathering, and support for administrative tasks, provided that human oversight is maintained. Conclusions: Individuals with OCD expressed nuanced views on AI in mental health care, balancing cautious optimism with several concerns. While AI may improve efficiency, standardization, and symptom monitoring, participants highlighted risks related to deindividualization, accuracy, and erosion of human connection. These findings underscore the importance of patient-centered, ethically guided AI integration that preserves the therapeutic alliance while leveraging technological benefits.

Indexed as

AIartificial intelligencechatbotlarge language modelLLMobsessive-compulsive disorderOCDpatient experiencequalitativethematic analysis

Identifiers

PMID42536834
PMCPMC13426896

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.