Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic
diagnosis. Materials and Methods In this systematic review, the PubMed database was …

Deep learning (DL) has the potential to transform medical diagnostics. However, the
diagnostic accuracy of DL is uncertain. Our aim was to evaluate the diagnostic accuracy of …

Radiomics refers to the extraction of mineable data from medical imaging and has been
applied within oncology to improve diagnosis, prognostication, and clinical decision support …

Using large, multi-national datasets for high-performance medical imaging AI systems
requires innovation in privacy-preserving machine learning so models can train on sensitive …

Importance Clinical artificial intelligence (AI) algorithms have the potential to improve clinical
care, but fair, generalizable algorithms depend on the clinical data on which they are trained …

Study Design Item 5. Indicate if the study is retrospective or prospective. Evaluate predictive
models in a prospective setting, if possible. Item 6. Define the study's goal, such as model …

In this review, we address the issue of fairness in the clinical integration of artificial
intelligence (AI) in the medical field. As the clinical adoption of deep learning algorithms, a …

An emphasis on overly broad notions of generalisability as it pertains to applications of
machine learning in health care can overlook situations in which machine learning might …

Artificial intelligence (AI) is expected to significantly influence the practice of medicine and
the delivery of healthcare in the near future. While there are only a handful of practical …

Breakthroughs in artificial intelligence (AI) hold enormous potential as it can automate
complex tasks and go even beyond human performance. In their study, McKinney et al. 1 …