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 …

Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …

Deep learning is a subdiscipline of artificial intelligence that uses a machine learning
technique called artificial neural networks to extract patterns and make predictions from …

This review provides the feasible literature on drug discovery through ML tools and
techniques that are enforced in every phase of drug development to accelerate the research …

Abstract Machine learning techniques have great potential to improve medical diagnostics,
offering ways to improve accuracy, reproducibility and speed, and to ease workloads for …

The clinical success of cancer immune checkpoint blockade (ICB) has refocused attention
on tumor-infiltrating lymphocytes (TILs) across cancer types. The outcome of immune …

Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical
care. Availability of high-dimensionality datasets coupled with advances in high …

Clinical workflows in oncology rely on predictive and prognostic molecular biomarkers.
However, the growing number of these complex biomarkers tends to increase the cost and …

Data processing and learning has become a spearhead for the advancement of medicine,
with pathology and laboratory medicine has no exception. The incorporation of scientific …

This paper investigates a deep learning method in image classification for the detection of
colorectal cancer with ResNet architecture. The exceptional performance of a deep learning …