Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Artificial Intelligence user interface preferences in radiology: A scoping review
5
Zitationen
7
Autoren
2025
Jahr
Abstract
Medical imaging AI user interface research is essential for the acceptability of AI technology into radiology departments. This scoping review identified the current landscape of AI user interface research within a radiology setting. There is a requirement for more radiology AI research focussing on end user or imaging professional involvement and their preferences. There is an explicit need for further research in the field, due to the lack of standardised outcome measures, lack clear findings regarding ideal user interfaces and lack of inclusion of radiographers. The dearth of studies including radiographers and small sample sizes of participants within these studies identifies the mindset shift required for radiology, and AI vendors alike.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.324 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.189 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.588 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 · 5.470 Zit.