Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Evaluation of End-User Participation in Artificial Intelligence Nursing Projects
0
Zitationen
4
Autoren
2024
Jahr
Abstract
Artificial Intelligence (AI) projects in healthcare, particularly in nursing, currently gain relevance but encounter challenges in user acceptance. Active participation of end-users in the development and implementation of AI can enhance acceptance. This study proposes a scale to measure the degree of end-user participation in AI development and implementation for nursing on the project level, rated by project managers. It employs the qualitative-analytical COARSE method for scale development and evaluation. The instrument includes 11 items across two sub-scales: activities for active participation of end-users and empowerment activities. It highlights the importance of the measurement's purpose and consequences for interpreting the results of the evaluated degree of end-user participation. The study points to future research opportunities, underscored by the need for psychometric validation, such as reliability and validity.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.260 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.116 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.493 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 · 5.438 Zit.