Evidence map›Paper›PMID 37314848›Full record

ArticleJournal of medical Internet research2023

Investigating the Impact of User Trust on the Adoption and Use of ChatGPT: Survey Analysis.

Avishek Choudhury, Hamid Shamszare

Registry-linked trialAbstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07631585 (Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients), which is not on this map. Cited by 91 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
91citing 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.

NCT07631585 recruitingnot on this mapstarted 2026, after this paper: background citation

Longitudinal Pre-Post Patient AI Trust Dynamics in Orthopedic Outpatients: A Mixed-Methods Observational Study With Matched Physician-Patient Dyads

TypeobservationalSponsorUtku GürhanRan2026 to 2027Enrolled180ConditionsPatient Health Information Seeking Behavior, Trust, Health Literacy, Orthopedics
3 · Its place in the literature

Who cites it

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

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  4. Artificial Intelligence Can Direct Patients Toward a Complaint-specific Musculoskeletal Provider.Journal of the American Academy of Orthopaedic Surgeons. Global research & reviews · 2026
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31 more citing papers are in PubMed but not listed here.

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

2 authors.

Avishek ChoudhuryIndustrial and Management Systems Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, United States.ORCID 0000-0002-5342-0709
Hamid ShamszareIndustrial and Management Systems Engineering, Benjamin M. Statler College of Engineering and Mineral Resources, West Virginia University, Morgantown, WV, United States.ORCID 0009-0007-7786-3452

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatGPT (Chat Generative Pre-trained Transformer) has gained popularity for its ability to generate human-like responses. It is essential to note that overreliance or blind trust in ChatGPT, especially in high-stakes decision-making contexts, can have severe consequences. Similarly, lacking trust in the technology can lead to underuse, resulting in missed opportunities.

objectiveThis study investigated the impact of users' trust in ChatGPT on their intent and actual use of the technology. Four hypotheses were tested: (1) users' intent to use ChatGPT increases with their trust in the technology; (2) the actual use of ChatGPT increases with users' intent to use the technology; (3) the actual use of ChatGPT increases with users' trust in the technology; and (4) users' intent to use ChatGPT can partially mediate the effect of trust in the technology on its actual use.

methodsThis study distributed a web-based survey to adults in the United States who actively use ChatGPT (version 3.5) at least once a month between February 2023 through March 2023. The survey responses were used to develop 2 latent constructs: Trust and Intent to Use, with Actual Use being the outcome variable. The study used partial least squares structural equation modeling to evaluate and test the structural model and hypotheses.

resultsIn the study, 607 respondents completed the survey. The primary uses of ChatGPT were for information gathering (n=219, 36.1%), entertainment (n=203, 33.4%), and problem-solving (n=135, 22.2%), with a smaller number using it for health-related queries (n=44, 7.2%) and other activities (n=6, 1%). Our model explained 50.5% and 9.8% of the variance in Intent to Use and Actual Use, respectively, with path coefficients of 0.711 and 0.221 for Trust on Intent to Use and Actual Use, respectively. The bootstrapped results failed to reject all 4 null hypotheses, with Trust having a significant direct effect on both Intent to Use (β=0.711, 95% CI 0.656-0.764) and Actual Use (β=0.302, 95% CI 0.229-0.374). The indirect effect of Trust on Actual Use, partially mediated by Intent to Use, was also significant (β=0.113, 95% CI 0.001-0.227).

conclusionsOur results suggest that trust is critical to users' adoption of ChatGPT. It remains crucial to highlight that ChatGPT was not initially designed for health care applications. Therefore, an overreliance on it for health-related advice could potentially lead to misinformation and subsequent health risks. Efforts must be focused on improving the ChatGPT's ability to distinguish between queries that it can safely handle and those that should be redirected to human experts (health care professionals). Although risks are associated with excessive trust in artificial intelligence-driven chatbots such as ChatGPT, the potential risks can be reduced by advocating for shared accountability and fostering collaboration between developers, subject matter experts, and human factors researchers.

Indexed as

Artificial IntelligenceTrustAdultHealth PersonnelHumansIntentionSurveys and QuestionnairesadoptionAI policyartificial intelligencebehavioral intentionchatbotChatGPThuman factorsintentshared accountabilitysurveytechnology adoptiontrusttrust in AI

Identifiers

PMID37314848
PMCPMC10337387

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.