Background: Artificial intelligence (AI) holds significant promise for transforming healthcare delivery, including dentistry. However, the successful integration of AI into dental practice necessitates an understanding of dental professionals' perspectives, attitudes, and readiness to adopt AI technology. This study aimed to explore dental professionals' perceptions, attitudes, and practices regarding AI adoption in dentistry.
Methods: This cross-sectional study was conducted among 256 dental professionals using an online questionnaire. Participants were assessed for familiarity with AI technology, perceived barriers to adoption, attitudes towards AI, current usage patterns, and factors influencing adoption decisions. Data are analysed using descriptive statistics, including frequencies, percentages, means, and standard deviations. Inferential statistics, such as chi-square tests and regression analysis, were employed to examine associations between variables and identify predictors of AI adoption in dentistry.
Results: The study surveyed 256 dental professionals from various regions across India, primarily aged 30 to 50 years (mean age: 42.6), with a nearly equal gender split (male: 48.4%, female: 51.6%) and high educational attainment (67.8% with master's or doctoral degrees). Private practices were predominant (56.3%). The diagnostic algorithms and treatment planning software were well known (77.3% and 70.3% familiarity, respectively). Technical concerns (average score: 3.82 ± 0.68) were the main barriers to AI adoption, followed by financial considerations (average score: 3.45 ± 0.72), ethical and legal issues (average score: 3.21 ± 0.65), and organizational factors (average score: 3.67 ± 0.71). Despite these concerns, most participants had positive attitudes towards AI (70.3% agreed). Current usage varied, with diagnostic support and administrative tasks being the most common (44.5% and 82.8% usage, respectively). Perceived utility (average score: 4.12 ± 0.75) and ease of use (average score: 3.98 ± 0.69) significantly influenced adoption, as identified by regression analysis (perceived utility: β = 0.342, p < 0.001; ease of use: β = 0.267, p = 0.005).
Conclusion: This study provides valuable insights into AI adoption in dentistry, highlighting the multifaceted nature of barriers and facilitators that influence dental professionals' adoption decisions. Strategies to promote AI adoption should address practical considerations, ethical concerns, and educational needs to facilitate the integration of AI technology into dental practices.
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http://dx.doi.org/10.7759/cureus.55107 | DOI Listing |
Med Care
January 2025
John Ware Research Group (JWRG), Watertown, MA.
Background: Comprehensive health-related quality of life (QOL) assessment under severe respondent burden constraints requires improved single-item scales for frequently surveyed domains. This article documents how new single-item-per-domain (SIPD) QOL General (QGEN-8) measures were constructed for domains common to SF-36 and results from the first psychometric tests comparing scores for the new measure in relation to those for the SF-36 profile and summary components.
Research Design: Online NORC surveys of adults, ages 19-93 (mean=52 y) representing the US population in 2020 (N=1648) included QGEN-8 and SF-36 items measuring physical (PF), social (SF), role physical (RP) and role emotional (RE) functioning and feelings of bodily pain (BP), vitality (VT), and mental health (MH).
PLoS One
January 2025
Department of Psychology, The Ohio State University, Columbus, Ohio, United States of America.
Food insecurity (FI), the lack of access to adequate food, is linked with negative health and psychological outcomes. FI is typically measured retrospectively over the last year; although this measurement is useful to understand FI prevalence to inform broad policy, it leaves the experience of FI in everyday life poorly understood. Understanding how FI varies across shorter periods of time (days or weeks) can help inform FI prevention and/or intervention.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemistry, New York University, New York, New York 10003, United States.
Molecular Docking is a critical task in structure-based virtual screening. Recent advancements have showcased the efficacy of diffusion-based generative models for blind docking tasks. However, these models do not inherently estimate protein-ligand binding strength thus cannot be directly applied to virtual screening tasks.
View Article and Find Full Text PDFBrain Spine
May 2024
Goettingen Medical University, Department of Trauma Surgery, Orthopedics and Plastic Surgery, Robert-Koch-Str. 40, D-37099, Goettingen, Germany.
Introduction: As medical education becomes more complex, the demand for advanced teaching and training methods has grown. Technological advancements have opened up new possibilities, particularly in the realm of virtual reality (VR) simulations for training.
Research Question: Our prospective, randomized pilot study aims to assess whether a novel VR-based 3D training platform can effectively teach the knowledge and skills needed for complex spinal surgery, specifically pedicle screw placement.
NEJM AI
October 2024
Google, Mountain View, CA, USA.
Background: Using artificial intelligence (AI) to interpret chest X-rays (CXRs) could support accessible triage tests for active pulmonary tuberculosis (TB) in resource-constrained settings.
Methods: The performance of two cloud-based CXR AI systems - one to detect TB and the other to detect CXR abnormalities - in a population with a high TB and human immunodeficiency virus (HIV) burden was evaluated. We recruited 1978 adults who had TB symptoms, were close contacts of known TB patients, or were newly diagnosed with HIV at three clinical sites.
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