Machine learning (ML) is increasingly used in clinical oncology to diagnose cancers, predict
patient outcomes, and inform treatment planning. Here, we review recent applications of ML …

Flexible electronics have recently gained considerable attention due to their potential to
provide new and innovative solutions to a wide range of challenges in various electronic …

The accelerated adoption of digital pathology and advances in deep learning have enabled
the development of robust models for various pathology tasks across a diverse array of …

With biotechnological advancements, innovative omics technologies are constantly
emerging that have enabled researchers to access multi-layer information from the genome …

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine
pathology slides in colorectal cancer (CRC). However, current approaches rely on …

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 …

Osteoporosis is caused by the disruption in homeostasis between bone formation and bone
resorption. Conventional management of osteoporosis involves systematic drug …

Background Artificial intelligence (AI) is rapidly fuelling a fundamental transformation in the
practice of pathology. However, clinical integration remains challenging, with no AI …

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology
slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other …

The histopathological phenotype of tumors reflects the underlying genetic makeup. Deep
learning can predict genetic alterations from pathology slides, but it is unclear how well …