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 …

Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

Background Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with
localized prostate cancer. However, ADT can negatively impact quality of life, and there …

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare
delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology …

Background Artificial intelligence (AI) has the potential to enhance diagnostic accuracy and
improve treatment outcomes. However, AI integration into clinical workflows and patient …

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant
treatment. The current gold standard of combined pathological and molecular profiling is …

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to
digital H&E images, and other histological image types, has begun to radically change how …

The NCCN Guidelines for Prostate Cancer include recommendations for staging and risk
assessment after a prostate cancer diagnosis and for the care of patients with localized …

Deep learning (DL), often called artificial intelligence (AI), has been increasingly used in
Pathology thanks to the use of scanners to digitize slides which allow us to visualize them on …

Artificial intelligence (AI) in oncology is advancing beyond algorithm development to
integration into clinical practice. This review describes the current state of the field, with a …