What factors predict user acceptance of ChatGPT for mental and physical healthcare: An extended technology acceptance model framework
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Abstract The rise of ChatGPT has emphasized the need for an improved conceptual understanding of users’ agency when interacting with artificial intelligence (AI) systems for healthcare. Australian ChatGPT users ( N = 216) completed a repeated measures online survey. Hierarchical regression analyses assessed the influence of demographic factors (age and gender), Technology Acceptance Model constructs (perceived usefulness and perceived ease of use), and extended variables (trust, privacy concerns) on users' behavioral intentions to use ChatGPT for physical and mental healthcare. The proposed model was partially supported: the findings emphasized the need to establish user trust in ChatGPT and its perceived usefulness in both areas of healthcare. Privacy concerns were a significant predictor of intentions to use ChatGPT for mental healthcare with perceived ease of use predicting intentions to use ChatGPT for physical healthcare. The findings indicate predictors of uses of AI cannot be generalized across healthcare types and unique drivers should be considered.
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Kelly et al. [41] investigated public acceptance of ChatGPT (Version 4o) for mental and physical healthcare and developed an extended TAM framework to explain users' intention to adopt conversational AI as a healthcare-support technology.
“Kelly et al. [41] investigated public acceptance of ChatGPT (Version 4o) for mental and physical healthcare and developed an extended TAM framework to explain users’ intention to adopt conversational AI as a healthcare-support technology.”
From Advancing Undergraduate Student Mental Healthcare of Social Anxiety Disorder: Evaluating the Acceptance of AR-Assisted Cognitive Behavioral Therapy Through TAM-Based Constructs. (2026) · cites this paper as [41]
Section 2.1 says the study used an extended TAM to predict intentions to use ChatGPT for mental and physical healthcare. Sections 3.1 and 3.3 describe an Australian convenience sample of people who had used ChatGPT, rather than the public at large. The copy does not identify the ChatGPT version studied as 4o (searched: GPT-4o and version). (LLM, conf=0.88)
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- Authors
- Kelly, S., Kaye, S.-A., White, K. M., & Oviedo-Trespalacios, O.
- Affiliations
- Queensland University of Technology (AU)
- Corresponding author
- Kelly, Sage
- Venue
- AI & SOCIETY
- Published
- 2025-05-03
- Volume
- 40
- Issue
- 8
- Pages
- 6257-6275
- ISSN
- 0951-5666 (ISSN-L)
- 1435-5655
- OpenAlex
- W4410052757
- Cited by
- 17
- References
- 117
- Citations by year
- 2026: 15 · 2025: 2
- FWCI
- 4.13
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- AI & Society (publishedVersion, cc-by)
- Funders
- Queensland University of Technology
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- ChatGPT, user acceptance, technology acceptance model, extended technology acceptance model, mental health services, trust in AI, privacy concerns, perceived usefulness, perceived ease of use, behavioral intention, generative AI in healthcare, user agency
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.
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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