Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Expectations and Limitations of Artificial Intelligence in Blood Cancer Diagnosis
0
Zitationen
4
Autoren
2026
Jahr
Abstract
In this commentary, we open the debate on what can be expected from artificial intelligence (AI) in the diagnosis of hematologic cancers. We discuss the key factors that make AI solutions robust, trustworthy, and, above all, generalizable, with particular emphasis on the importance of dataset quality in shaping the performance and effectiveness of AI models.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.911 Zit.
pROC: an open-source package for R and S+ to analyze and compare ROC curves
2011 · 13.762 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.458 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 12.052 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.387 Zit.