Environmental cleaning and disinfection in the operating room: a systematic scoping review through a human factors and systems engineering lens.

Objective: To synthesize evidence and identify gaps in the literature on environmental cleaning and disinfection in the operating room based on a human factors and systems engineering approach guided by the Systems Engineering Initiative for Patient Safety (SEIPS) model.

Design: A systematic scoping review.

Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched 4 databases (ie, PubMed, EMBASE, OVID, CINAHL) for empirical studies on operating-room cleaning and disinfection.

Improving Recognition of Defective Epoxy Images in Integrated Circuit Manufacturing by Data Augmentation.

This paper discusses the problem of recognizing defective epoxy drop images for the purpose of performing vision-based die attachment inspection in integrated circuit (IC) manufacturing based on deep neural networks. Two supervised and two unsupervised recognition models are considered. The supervised models examined are an autoencoder (AE) network together with a multi-layer perceptron network (MLP) and a VGG16 network, while the unsupervised models examined are an autoencoder (AE) network together with k-means clustering and a VGG16 network together with k-means clustering.

Generating Defective Epoxy Drop Images for Die Attachment in Integrated Circuit Manufacturing via Enhanced Loss Function CycleGAN.

In integrated circuit manufacturing, defects in epoxy drops for die attachments are required to be identified during production. Modern identification techniques based on vision-based deep neural networks require the availability of a very large number of defect and non-defect epoxy drop images. In practice, however, very few defective epoxy drop images are available.

Examining the effect of explanation on satisfaction and trust in AI diagnostic systems.

Background: Artificial Intelligence has the potential to revolutionize healthcare, and it is increasingly being deployed to support and assist medical diagnosis. One potential application of AI is as the first point of contact for patients, replacing initial diagnoses prior to sending a patient to a specialist, allowing health care professionals to focus on more challenging and critical aspects of treatment. But for AI systems to succeed in this role, it will not be enough for them to merely provide accurate diagnoses and predictions.