Various forms of artificial intelligence (AI) applications are being deployed and used in many
healthcare systems. As the use of these applications increases, we are learning the failures …

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to
a readily accessible tool for cancer researchers. AI-based tools can boost research …

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a
driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted …

Clinical prediction models follow a standard development pipeline: model development and
internal validation; external validation; and clinical impact studies. External validation …

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past
decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of …

Purpose To externally test four chest radiograph classifiers on a large, diverse, real-world
dataset with robust subgroup analysis. Materials and Methods In this retrospective study …

Background Automated interpretation of normal chest radiographs could alleviate the
workload of radiologists. However, the performance of such an artificial intelligence (AI) tool …

Artificial intelligence (AI) algorithms are prone to bias at multiple stages of model
development, with potential for exacerbating health disparities. However, bias in imaging AI …

The rapid growth of medical imaging has placed increasing demands on radiologists. In this
scenario, artificial intelligence (AI) has become an attractive partner, one that may …

Artificial intelligence (AI) tools may assist breast screening mammography programs, but
limited evidence supports their generalizability to new settings. This retrospective study used …