Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
363TiP Pseudo-anonymized non-interventional retrospective clinical validation of AI-ambient patient-clinical intelligence (A.P.C.I.) using the GRADE methodology
0
Zitationen
6
Autoren
2025
Jahr
Abstract
Artificial intelligence (AI) is rapidly transforming clinical decision-making in oncology, particularly in imaging, pathology, and study design. However, generating a personalized, evidence-based diagnosis and treatment pathway remains a challenge, often influenced by subjective judgment and empirical practices. In contrast, the GRADE (Grading of Recommendations, Assessment, Development and Evaluation) approach has gained WHO recognition as a transparent and systematic methodology for evidence assessment and recommendation development.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.239 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.095 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.463 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 · 5.428 Zit.