Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Enhancing Clinician Trust in AI Diagnostics: A Dynamic Framework for Confidence Calibration and Transparency
16
Zitationen
11
Autoren
2025
Jahr
Abstract
These findings suggest that enhanced transparency and confidence calibration can substantially reduce override rates and promote clinician acceptance of AI diagnostics. Future work should focus on clinical validation to optimize patient safety, diagnostic accuracy, and efficiency.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.652 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.567 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 8.083 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.856 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.