Advances in digitizing tissue slides and the fast-paced progress in artificial intelligence,
including deep learning, have boosted the field of computational pathology. This field holds …

The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

Deep learning has recently demonstrated considerable promise for a variety of computer
vision tasks. However, in many practical applications, large-scale labeled datasets are not …

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

Failure to recognize samples from the classes unseen during training is a major limitation of
artificial intelligence in the real-world implementation for recognition and classification of …

Simple Summary Lung cancer is one of the most common and deadly malignancies
worldwide. Microscopic examination of histological and cytological lung specimens can be a …

Computational pathology is currently witnessing a surge in the development of AI
techniques, offering promise for achieving breakthroughs and significantly impacting the …

The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Integrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to
revolutionise clinical practice and research. Artificial intelligence harnesses advanced …

A clinical artificial intelligence (AI) system is often validated on data withheld during its
development. This provides an estimate of its performance upon future deployment on data …