This study explores whether labeling AI as either "trustworthy" or "reliable" influences user perceptions and acceptance of automotive AI technologies. Utilizing a one-way between-subjects design, the research presented online participants (N = 478) with a text presenting guidelines for either trustworthy or reliable AI, before asking them to evaluate 3 vignette scenarios and fill in a modified version of the Technology Acceptance Model which covers different variables, such as perceived ease of use, human-like trust, and overall attitude. While labeling AI as "trustworthy" did not significantly influence people's judgements on specific scenarios, it increased perceived ease of use and human-like trust, namely benevolence, suggesting a facilitating influence on usability and an anthropomorphic effect on user perceptions. The study provides insights into how specific labels affect adopting certain perceptions of AI technology.
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http://dx.doi.org/10.1038/s41598-025-85558-2 | DOI Listing |
Sci Rep
January 2025
Faculty of Philosophy, Philosophy of Science and the Study of Religion, Ludwig-Maximilians-Universität München, Munich, Germany.
This study explores whether labeling AI as either "trustworthy" or "reliable" influences user perceptions and acceptance of automotive AI technologies. Utilizing a one-way between-subjects design, the research presented online participants (N = 478) with a text presenting guidelines for either trustworthy or reliable AI, before asking them to evaluate 3 vignette scenarios and fill in a modified version of the Technology Acceptance Model which covers different variables, such as perceived ease of use, human-like trust, and overall attitude. While labeling AI as "trustworthy" did not significantly influence people's judgements on specific scenarios, it increased perceived ease of use and human-like trust, namely benevolence, suggesting a facilitating influence on usability and an anthropomorphic effect on user perceptions.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Euro-Mediterranean University of Fez (UEMF), Fez, Fez, Morocco.
Background: The spread of fake news may lead to a disparate wave of digital health-seeking behavior, cyberchondria, anxiety, indecision, and other psychosocial dysfunctions, including collapse in social capital and stigmatization. In this study, we utilized a bibliometric analysis to discern the primary trends associated with health communication and health-seeking behavior regarding dementia-related contents in countries within the Middle East and North African (MENA) region.
Method: A literature review was conducted in November 2023.
BioData Min
January 2025
Fondazione Bruno Kessler, Trento, Italy.
Biomedical datasets are the mainstays of computational biology and health informatics projects, and can be found on multiple data platforms online or obtained from wet-lab biologists and physicians. The quality and the trustworthiness of these datasets, however, can sometimes be poor, producing bad results in turn, which can harm patients and data subjects. To address this problem, policy-makers, researchers, and consortia have proposed diverse regulations, guidelines, and scores to assess the quality and increase the reliability of datasets.
View Article and Find Full Text PDFInt J Med Inform
January 2025
IRCCS Ospedale Galeazzi - Sant'Ambrogio, Milano, Italy.
Background: One of the main challenges in the maintenance of registries is to keep a high follow-up rate and a reliable strategy to limit dropout is currently lacking. Aim of this study was to utilize machine learning (ML) models to highlight the characteristics of patients who are most likely to drop out, and to evaluate the potential cost effectiveness of the implementation of a follow-up system based on the obtained data.
Methods: All patients recruited in the local spine surgery registry were included and demographic, peri- and postoperative data were collected.
Int J Med Inform
December 2024
Neurosurgery Department, Hamad General Hospital, Qatar; Department of Clinical Academic Sciences, College of Medicine, Qatar University, Doha, Qatar; Department of Neurological Sciences, Weill Cornell Medicine, Doha, Qatar.
Introduction: Artificial Intelligence is in the phase of health care, with transformative innovations in diagnostics, personalized treatment, and operational efficiency. While having potential, critical challenges are apparent in areas of safety, trust, security, and ethical governance. The development of these challenges is important for promoting the responsible adoption of AI technologies into healthcare systems.
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