Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience.

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use.

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

The structure of a transcribing T7 RNA polymerase in transition from initiation to elongation.

Structural studies of the T7 bacteriophage DNA-dependent RNA polymerase (T7 RNAP) have shown that the conformation of the amino-terminal domain changes substantially between the initiation and elongation phases of transcription, but how this transition is achieved remains unclear. We report crystal structures of T7 RNAP bound to promoter DNA containing either a 7- or an 8-nucleotide (nt) RNA transcript that illuminate intermediate states along the transition pathway. The amino-terminal domain comprises the C-helix subdomain and the promoter binding domain (PBD), which consists of two segments separated by subdomain H.