Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Radiology Synthetic Confusion: How Generative Artificial Intelligence Amplifies Misunderstandings of Radiologists and Technologists in Patient-Facing Media
1
Zitationen
5
Autoren
2025
Jahr
Abstract
Generative AI frequently misrepresents radiologist roles and demographics, reinforcing stereotypes and public confusion. Greater oversight and inclusion standards are needed to ensure equitable AI-generated healthcare content.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.560 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.451 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.948 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.797 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.