Artificial intelligence (AI) has become an integral part of many contemporary technologies, such as social media platforms, smart devices, and global logistics systems. At the same time, research on the public acceptance of AI shows that many people feel quite apprehensive about the potential of such technologies-an observation that has been connected to both demographic and sociocultural user variables (e.g., age, previous media exposure). Yet, due to divergent and often ad-hoc measurements of AI-related attitudes, the current body of evidence remains inconclusive. Likewise, it is still unclear if attitudes towards AI are also affected by users' personality traits. In response to these research gaps, we offer a two-fold contribution. First, we present a novel, psychologically informed questionnaire (ATTARI-12) that captures attitudes towards AI as a single construct, independent of specific contexts or applications. Having observed good reliability and validity for our new measure across two studies (N = 490; N = 150), we examine several personality traits-the Big Five, the Dark Triad, and conspiracy mentality-as potential predictors of AI-related attitudes in a third study (N = 298). We find that agreeableness and younger age predict a more positive view towards artificially intelligent technology, whereas the susceptibility to conspiracy beliefs connects to a more negative attitude. Our findings are discussed considering potential limitations and future directions for research and practice.
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http://dx.doi.org/10.1038/s41598-024-53335-2 | DOI Listing |
J Med Radiat Sci
December 2024
Centre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, New South Wales, Australia.
Introduction: The application of artificial intelligence (AI) in radiation therapy holds promise for addressing challenges, such as healthcare staff shortages, increased efficiency and treatment planning variations. Increased AI adoption has the potential to standardise treatment protocols, enhance quality, improve patient outcomes, and reduce costs. However, drawbacks include impacts on employment and algorithmic biases, making it crucial to navigate trade-offs.
View Article and Find Full Text PDFBJR Open
January 2024
Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, MA 02115, United States.
The use of artificial intelligence (AI) holds great promise for radiation oncology, with many applications being reported in the literature, including some of which are already in clinical use. These are mainly in areas where AI provides benefits in efficiency (such as automatic segmentation and treatment planning). Prediction models that directly impact patient decision-making are far less mature in terms of their application in clinical practice.
View Article and Find Full Text PDFJAMA Netw Open
November 2024
Radiology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.
Importance: Understanding the association of artificial intelligence (AI) with physician burnout is crucial for fostering a collaborative interactive environment between physicians and AI.
Objective: To estimate the association between AI use in radiology and radiologist burnout.
Design, Setting, And Participants: This cross-sectional study conducted a questionnaire survey between May and October 2023, using the national quality control system of radiology in China.
Clin Chem Lab Med
October 2024
Department of Medicine (DIMED), University of Padova and University Hospital of Padova, Padova, Italy.
Background: As the healthcare sector evolves, Artificial Intelligence's (AI's) potential to enhance laboratory medicine is increasingly recognized. However, the adoption rates and attitudes towards AI across European laboratories have not been comprehensively analyzed. This study aims to fill this gap by surveying European laboratory professionals to assess their current use of AI, the digital infrastructure available, and their attitudes towards future implementations.
View Article and Find Full Text PDFBMC Med Educ
October 2024
Institute for Social Research, Survey Research Center, University of Michigan, Ann Arbor, USA.
Background: Artificial intelligence (AI) is transforming health profession education (HPE) through personalized learning technologies. HPE students must also learn about AI to understand its impact on healthcare delivery. We examined HPE students' AI-related knowledge and attitudes, and perceived challenges in integrating AI in HPE.
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