Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluation of Attitudes and Perceptions in Students About the Use of Artificial Intelligence in Craniomaxillofacial Surgery
2
Zitationen
3
Autoren
2024
Jahr
Abstract
Developments in technology have created great changes in the field of medicine and dentistry. Artificial intelligence technology is one of the most important innovations that caused this change. This study aimed to evaluate the opinions of dentistry students regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Two hundred ninety-six dentistry students between the ages of 19 and 30 participated in the study. Participants submitted the survey by e-mail examining the student's opinions and attitudes regarding the use of artificial intelligence in dentistry and craniomaxillofacial surgery. Respondents' anonymity was ensured. 47.30% (n: 140) of the students participating in the study are fourth-year students, and 52.70% (n: 156) are fifth-year students. While 48.98% (n: 145) of the participants have knowledge about the uses of artificial intelligence in daily life, 28.37% (n: 84) of the students have knowledge about robotic surgery. While ~74% of the participants think that artificial intelligence will improve the field of dentistry and craniomaxillofacial surgery, it has been observed that they are not worried about these applications replacing dentists in the future. It was determined that there was no statistically significant difference between fourth-year and fifth-year students in their knowledge levels about the areas of use of artificial intelligence ( P =0.548). Students' opinions show that 74% agree that artificial intelligence will lead to major advances in the field of dentistry and craniomaxillofacial surgery. This shows the relationship between dentists and artificial intelligence points to a bright future.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.292 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.143 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.539 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.452 Zit.