Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it
will improve health care and reduce costs. Has AI been able to fulfill that promise? We …

Objectives Map the current landscape of commercially available artificial intelligence (AI)
software for radiology and review the availability of their scientific evidence. Methods We …

Purpose Despite tremendous gains from deep learning and the promise of artificial
intelligence (AI) in medicine to improve diagnosis and save costs, there exists a large …

Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be
practiced in the near future, but several issues need to be resolved before AI can be widely …

The pace of regulatory clearance of artificial intelligence (AI) algorithms for radiology
continues to accelerate, and numerous algorithms are becoming available for use in clinical …

Advances in computing hardware and software platforms have led to the recent resurgence
in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for …

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 …

Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and
growing rapidly, fueled by availability of large datasets (“big data”), substantial advances in …

As the role of artificial intelligence (AI) in clinical practice evolves, governance structures
oversee the implementation, maintenance, and monitoring of clinical AI algorithms to …

Introduction Artificial intelligence (AI) has started to be increasingly adopted in medical
imaging and radiotherapy clinical practice, however research, education and partnerships …