Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
AI in radiology and interventions: a structured narrative review of workflow automation, accuracy, and efficiency gains of today and what’s coming
0
Zitationen
1
Autoren
2025
Jahr
Abstract
AI has already demonstrated measurable gains in diagnostic accuracy, efficiency, and workflow standardization. Interventional applications are emerging, with future growth expected in guidance, robotics, and real-time optimization. Despite progress, key limitations include algorithm generalizability, clinical interpretability, organizational readiness, and regulatory uncertainty. AI will augment rather than replace human expertise, with collaborative human-AI workflows being essential. Future integration efforts must address interoperability, workforce adaptation, and ethical considerations to ensure safe, equitable, and clinically impactful deployment.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.231 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.084 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.444 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 · 5.423 Zit.