Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Overcoming the challenges to implementation of artificial intelligence in pathology
65
Zitationen
2
Autoren
2023
Jahr
Abstract
Pathologists worldwide are facing remarkable challenges with increasing workloads and lack of time to provide consistently high-quality patient care. The application of artificial intelligence (AI) to digital whole-slide images has the potential of democratizing the access to expert pathology and affordable biomarkers by supporting pathologists in the provision of timely and accurate diagnosis as well as supporting oncologists by directly extracting prognostic and predictive biomarkers from tissue slides. The long-awaited adoption of AI in pathology, however, has not materialized, and the transformation of pathology is happening at a much slower pace than that observed in other fields (eg, radiology). Here, we provide a critical summary of the developments in digital and computational pathology in the last 10 years, outline key hurdles and ways to overcome them, and provide a perspective for AI-supported precision oncology in the future.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.918 Zit.
pROC: an open-source package for R and S+ to analyze and compare ROC curves
2011 · 13.769 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.468 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 12.061 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.396 Zit.