Artificial intelligence (AI) is rapidly emerging in healthcare, yet applications in surgery
remain relatively nascent. Here we review the integration of AI in the field of surgery …

This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

Every year, cancer is responsible for millions of deaths worldwide and, even though much
progress has been achieved in medicine, there are still many issues that must be addressed …

Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale
extracted imaging information to clinical and biological endpoints. The development of …

Medical imaging can assess the tumor and its environment in their entirety, which makes it
suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in …

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated
remarkable progress in image-recognition tasks. Methods ranging from convolutional neural …

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use
of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on …

Judgement, as one of the core tenets of medicine, relies upon the integration of multilayered
data with nuanced decision making. Cancer offers a unique context for medical decisions …

Radiomics, the high-throughput mining of quantitative image features from standard-of-care
medical imaging that enables data to be extracted and applied within clinical-decision …

Introduction Immunotherapy is regarded as one of the major breakthroughs in cancer
treatment. Despite its success, only a subset of patients responds—urging the quest for …