Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Toward a human‐centric co‐design methodology for AI detection of differences between planned and delivered dose in radiotherapy
1
Zitationen
12
Autoren
2025
Jahr
Abstract
Using a co-creation strategy, which is a novel approach in RT, we were able to prototype a novel human-interpretable interface to detect RT treatment errors and aid the DGRT workflow. Users showed confidence that the overall design method and the proposed prototype could lead to a viable clinical implementation.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.214 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.071 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.429 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 · 5.418 Zit.