Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Knowledge and perceptions of Moroccan medical physicists regarding the contribution of artificial intelligence in medical imaging and radiotherapy
3
Zitationen
5
Autoren
2025
Jahr
Abstract
To explore the perception of Moroccan medical physicists regarding the use of Artificial Intelligent (AI) in medical imaging and in radiotherapy. A standardized anonymous questionnaire of 24 questions was sent to our target population, medical physics PhDs (G1), medical physics PhD students (G2) and Master's students in medical physics (G3). It covers their knowledge and skills in artificial intelligence, their training in the field as well as their practices, and the threats and limits of AI. The three groups shared almost the same opinions on the training program for medical physicists and that more than 87.50% of the three groups thought that AI should be taught in their training program. Over 81.3% of the three groups share the same opinion regarding the role of AI in medical physicists. They strongly agreed or agreed that they were ready to learn and apply AI in their practice. In addition, 50% of G1s, 68.8% of G2s, and 87.5% of G3s strongly agree or agree that more and more tasks, such as quality control and treatment planning, will be performed by AI. Furthermore, 62.5% of G1s, 81.3% of G2s, and 75.0% of G3s strongly agree or agree that AI solutions will make it possible to considerably reduce radiation doses in the field of imaging in the next few years. The reinforcement of continuous training and the introduction of training modules in the curriculum of Moroccan medical physicists’ as well as a broad awareness of the benefits of AI are guarantees for the successful implementation of this innovative technology.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.287 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.140 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.534 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 · 5.450 Zit.