In ophthalmology, extensive use of ancillary imaging has enabled the development of
artificial intelligence models, for which data are crucial. A data-sharing environment …

Background Imaging plays a pivotal role in eye assessment. With the introduction of
advanced machine learning and artificial intelligence (AI), the focus has shifted to imaging …

Changes in the data distribution at test time can have deleterious effects on the performance
of predictive models $ p (y| x) $. We consider situations where there are additional meta …

Integration of multi-modal imaging techniques and various cancer treatments based on their
respective characteristics would be beneficial for enhancing anticancer efficacy. Exploiting …

Diversity, equity, and inclusion (DEI) is both a critical ingredient and moral imperative in
shaping the future of radiology artificial intelligence (AI) for improved patient care, from …

Featured Application Investigation into whether and how much AI-based heat-maps can
assist radiologists when making diagnoses based on medical images. Abstract In modern …

Summary Background Chest X-rays (CXR) are essential for diagnosing a variety of
conditions, but when used on new populations, model generalizability issues limit their …

Abstract Purpose of Review Bias in artificial intelligence (AI) models can result in unintended
consequences. In cardiovascular imaging, biased AI models used in clinical practice can …

La radiologie est l'un des premiers secteurs médicaux à être concerné concrètement par
l'arrivée de dispositifs labellisés «intelligence artificielle» pour le traitement des images …

Summary In this work, we (1) outline the design process of clinical ML/AI tools;(2) identify
several key design questions one must consider when developing such a tool, including …