Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Availability and transparency of artificial intelligence models in radiology: a meta-research study
2
Zitationen
5
Autoren
2025
Jahr
Abstract
Question The study addresses the limited availability of AI models in radiology, especially DL models, which impacts external validation and clinical reliability. Findings Only 39.9% of radiology AI studies made their models available, with DL models showing particularly low availability at 11.5%. Clinical relevance Improving the availability of radiology AI models is essential for enabling external validation, ensuring reliable clinical application, and advancing patient care by fostering robust and transparent AI systems.
Ähnliche Arbeiten
New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1)
2008 · 28.885 Zit.
TNM Classification of Malignant Tumours
1987 · 16.123 Zit.
A survey on deep learning in medical image analysis
2017 · 13.563 Zit.
Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening
2011 · 10.762 Zit.
The American Joint Committee on Cancer: the 7th Edition of the AJCC Cancer Staging Manual and the Future of TNM
2010 · 9.107 Zit.