DOME: recommendations for supervised machine learning validation in biology | Nature
Methods Skip to main content Thank you for visiting nature.com. You are using a browser version …

The presence of confounding effects (or biases) is one of the most critical challenges in
using deep learning to advance discovery in medical imaging studies. Confounders affect …

The rise of digital data and computing power have contributed to significant advancements
in artificial intelligence (AI), leading to the use of classification and prediction models in …

Background It is believed that artificial intelligence (AI) will be an integral part of health care
services in the near future and will be incorporated into several aspects of clinical care such …

The past decade in rheumatology has seen tremendous innovation in digital health
technologies, including the electronic health record, virtual visits, mobile health, wearable …

Stroke ranks among the leading causes for morbidity and mortality worldwide. New and
continuously improving treatment options such as thrombolysis and thrombectomy have …

Background Significant efforts have been made to develop artificial intelligence (AI)
solutions for health care improvement. Despite the enthusiasm, health care professionals …

Artificial intelligence (AI) refers to machines that are trained to perform tasks associated with
human intelligence, interpret external data, learn from that external data, and use that …

Background Machine learning (ML) is a growing field in medicine. This narrative review
describes the current body of literature on ML for clinical decision support in infectious …

AI applications hold great potential for improving healthcare. However, successfully
operating AI is a complex endeavor requiring organizations to establish adequate …