… Historically, artificially intelligent systems applied handcrafted rules to the specific tasks
they were meant to solve. Each task required domain-specific knowledge, engineering and …

… However, although the end of today's radiologists' profession has been proclaimed multiple
times, routine clinical application of such AI algorithms in 2020 remains rare. The goal of this …

… To date, most clinical applications of AI for the COVID-19 response have focused on
diagnosis based on medical imaging, with an increasing number of studies exploring non-invasive …

… to clinical evaluation of artificial intelligence tools for use in medical diagnosis and prediction,
mainly from the standpoints of clinical … for developing artificial intelligence algorithms for …

Artificial intelligence (AI)-based algorithms are rapidly entering the health care field and
have the potential to improve patient care. Our article focuses on the use of autonomous AI …

… Most models and tools do not use terminology and data model standards, which makes the
transition to clinical applications in a clinical setting difficult and impairs interoperability with …

… clinical data and questions. In this article, we provide an overview of the core concepts of
artificial intelligence… of how artificial intelligence can be harnessed to support clinical decisions. …

… In the second part, we provide a concise overview of the main clinical applications, focusing
on the liver, breast, thyroid, lymph nodes, and bowel. For each application, we report some …

… Artificial Intelligence (AI) solutions that enable expeditious and streamlined clinical research.
In this paper, we identified opportunities, challenges, and potential implications of AI in CTs. …

… In our view, for clinicians to successfully harness the power of artificial intelligence (AI) as
an integral part of the clinical decision-making framework, they should learn to see uncertainty …