Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review.

The application of machine learning (ML) and artificial intelligence (AI) in healthcare domains has received much attention in recent years, yet significant questions remain about how these new tools integrate into frontline user workflow, and how their design will impact implementation. Lack of acceptance among clinicians is a major barrier to the translation of healthcare innovations into clinical practice. In this systematic review, we examine when and how clinicians are consulted about their needs and desires for clinical AI tools.

Using a human factors-centric approach to development and testing of a face shield designed for health care workers: A COVID-19 case study for process and outcomes.

Background: Face shields are a critical piece of personal protective equipment and their comfort impacts compliant use and thus protectiveness. Optimal design criteria for face shield use in healthcare environments are limited. We attempt to identify factors affecting face shield usability and to test and optimize a face shield for comfort and function in health care settings.