Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
AI in Fracture Detection: A Cross-Disciplinary Analysis of Physician Acceptance Using the UTAUT Model
5
Zitationen
11
Autoren
2025
Jahr
Abstract
: Physicians' intention to use AI fracture detection is primarily influenced by perceived usefulness and ease of use. Implementation strategies should focus on intuitive design, targeted training, and clear communication of clinical benefits. Further research should evaluate post-implementation usage and user satisfaction.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.663 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.576 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.091 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.859 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.