As more artificial intelligence (AI) applications are integrated into healthcare, there is an
urgent need for standardization and quality-control measures to ensure a safe and …

Despite artificial intelligence (AI) technology progresses at unprecedented rate, our ability to
translate these advancements into clinical value and adoption at the bedside remains …

Explainable artificial intelligence (XAI) has emerged as a promising solution for addressing
the implementation challenges of AI/ML in healthcare. However, little is known about how …

Exposure to research data and artificial intelligence (AI) model predictions may lead to many
sources of bias in clinical decision-making and model evaluation. These include anchoring …

Artificial intelligence (AI) holds significant promise for revolutionizing health care by
enhancing diagnosis, treatment, and patients' safety. However, the current disparity between …

© Annals of Translational Medicine. All rights reserved. Ann Transl Med 2022; 10 (18): 950|
https://dx. doi. org/10.21037/atm-22-4203 with a modifiable degree of fidelity for a richer …

Artificial intelligence (AI) has been the subject of considerable interest for many years for its
potential to improve clinical care—yet its actual impact on patient outcomes when deployed …

Objective To derive a comprehensive implementation framework for clinical AI models within
hospitals informed by existing AI frameworks and integrated with reporting standards for …

The potential presented by Artificial Intelligence (AI) for healthcare has long been
recognised by the technical community. More recently, this potential has been recognised by …

The use of artificial intelligence (AI) in healthcare is transforming a number of medical fields,
including nephrology. The integration of various AI techniques in nephrology facilitates the …