The increasing availability of data and computing power has made machine learning (ML) a
viable approach to faster, more efficient healthcare delivery. To exploit the potential of data …

Introduction The increasing availability of data and computing power has made machine
learning (ML) a viable approach to faster, more efficient healthcare delivery. Methods A …

Objectives Machine-learning (ML) consist of developing algorithms that can generate output
predictions based on learning from input data. This review aims to synthesize available …

Medical science in general and oncology in particular are dynamic, rapidly evolving
subjects. Brain and spine tumors, whether primary or secondary, constitute a significant …

Background Machine learning (ML) is an application of artificial intelligence (AI) that gives
computer systems the ability to learn data, without being explicitly programmed. Currently …

Purpose The advent of clinically adapted machine learning algorithms can solve numerous
problems ranging from disease diagnosis and prognosis to therapy recommendations. This …

Although advances in machine-learning healthcare applications promise great potential for
innovative medical care, few data are available on the translational status of these new …

We present a synopsis of publications focused on machine learning (ML) or artificial
intelligence (AI) applications in healthcare for the year 2019. We appreciate the work of …

A crucial area of study called pharmaco-epidemiology combines pharmacology and
epidemiology to investigate how pharmaceuticals are used, work, and if they are safe in a …

The aptness of machine learning (ML) to learn from large datasets, discover trends, and
make predictions has demonstrated its potential to metamorphose the medical field. Medical …