Understanding Public Trust in AI-Based Clinical Psychological Decision-Making: A Qualitative Study
Abstract
Background: Artificial intelligence (AI) is increasingly being applied within healthcare and has the potential to support clinical decision-making and improve the efficiency of healthcare delivery. However, concerns surrounding the use of AI in healthcare may influence public trust in AI-generated diagnoses and treatment decisions. These concerns may be particularly important within clinical psychological settings, where decisions can involve sensitive personal information and significant implications for patients. Methods: This qualitative study explored factors influencing public trust in AI-based decision-making within a clinical psychological context. In-depth semi-structured interviews were conducted to explore participants' perceptions of AI and the factors that may influence their willingness to trust AI-generated decisions. Interview data were analysed using deductive thematic analysis. Particular attention was given to perceptions of transparency, system design, bias, privacy, regulation, and responsibility. Participants' perspectives on AI functioning as an assistant to a healthcare professional compared with AI operating as an independent decision-making tool were also explored. Results: Several factors were identified as influencing trust in AI-based clinical psychological decision-making, including transparency, system design, perceived bias, privacy, regulation, and responsibility for AI-generated decisions. Participants also distinguished between AI used as an assistant to healthcare professionals and AI operating independently, indicating that the perceived role and level of human involvement may influence trust in AI decisions. The findings additionally highlighted the importance of public understanding of both AI and healthcare when considering the acceptance of AI-based clinical decision-making. Conclusions: Public trust in AI-based clinical psychological decision-making appears to be influenced by a combination of technological, ethical, legal, and human factors. Transparency, perceived bias, privacy protections, appropriate regulation, and clarity regarding responsibility may be important considerations in the development and implementation of AI within clinical psychological settings. The findings also indicate a potential need for greater public education and literacy regarding both AI and healthcare. Clear ethical and legal frameworks may further support public confidence in the responsible use of AI in clinical decision-making.
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BibTeX
@misc{lei2026understanding,
title = {{Understanding Public Trust in AI-Based Clinical Psychological Decision-Making: A Qualitative Study}},
author = {Yangqiong Lei and Samuel Bennett},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/u96y5_v1},
url = {https://osf.io/preprints/psyarxiv/u96y5_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Understanding Public Trust in AI-Based Clinical Psychological Decision-Making: A Qualitative Study
AU - Yangqiong Lei
AU - Samuel Bennett
PY - 2026
DA - 2026/09/21
PB - PsyArXiv
DO - 10.31234/osf.io/u96y5_v1
UR - https://osf.io/preprints/psyarxiv/u96y5_v1/
AB - Background: Artificial intelligence (AI) is increasingly being applied within healthcare and has the potential to support clinical decision-making and improve the efficiency of healthcare delivery. However, concerns surrounding the use of AI in healthcare may influence public trust in AI-generated diagnoses and treatment decisions. These concerns may be particularly important within clinical psychological settings, where decisions can involve sensitive personal information and significant implications for patients. Methods: This qualitative study explored factors influencing public trust in AI-based decision-making within a clinical psychological context. In-depth semi-structured interviews were conducted to explore participants' perceptions of AI and the factors that may influence their willingness to trust AI-generated decisions. Interview data were analysed using deductive thematic analysis. Particular attention was given to perceptions of transparency, system design, bias, privacy, regulation, and responsibility. Participants' perspectives on AI functioning as an assistant to a healthcare professional compared with AI operating as an independent decision-making tool were also explored. Results: Several factors were identified as influencing trust in AI-based clinical psychological decision-making, including transparency, system design, perceived bias, privacy, regulation, and responsibility for AI-generated decisions. Participants also distinguished between AI used as an assistant to healthcare professionals and AI operating independently, indicating that the perceived role and level of human involvement may influence trust in AI decisions. The findings additionally highlighted the importance of public understanding of both AI and healthcare when considering the acceptance of AI-based clinical decision-making. Conclusions: Public trust in AI-based clinical psychological decision-making appears to be influenced by a combination of technological, ethical, legal, and human factors. Transparency, perceived bias, privacy protections, appropriate regulation, and clarity regarding responsibility may be important considerations in the development and implementation of AI within clinical psychological settings. The findings also indicate a potential need for greater public education and literacy regarding both AI and healthcare. Clear ethical and legal frameworks may further support public confidence in the responsible use of AI in clinical decision-making.
ER -