Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Advances in artificial intelligence for gynecological imaging: technical bottlenecks and future engineering solutions
0
Zitationen
4
Autoren
2025
Jahr
Abstract
Artificial intelligence (AI) has shown significant promise in gynecological imaging, particularly in diagnosing gynecological cancers and benign diseases. However, the application of AI in this field faces several technical challenges, including data quality, model generalizability and clinical interpretability. This paper reviews the current state of AI in gynecological imaging, identifies key technical bottlenecks, and propose future engineering solutions. Through case studies, we highlight the potential and challenges of AI in Gynecological diagnosis, providing a roadmap for future research directions. We emphasize the importance of data sharing, model optimization, and clinical validation to overcome these challenges and enhance the integration of AI into clinical practice.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.739 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.327 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 11.922 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.289 Zit.
Radiomics: Images Are More than Pictures, They Are Data
2015 · 8.077 Zit.