Integration of Remote Patient Monitoring Systems into Physicians Work in Underserved Communities: Survey of Healthcare Provider Perspectives

Remote patient monitoring (RPM) technologies have been identified as a viable alternative to improve access to care in underserved communities. Successful RPM platforms are designed and implemented for seamless integration into healthcare providers work to increase adoption and availability for offering remote care. A quantitative survey was designed and administered to elicit perspectives from a wide range of stakeholders, including healthcare providers and healthcare administrators, about barriers and facilitators in the adoption and integration of RPM into clinical workflows in underserved areas. Ease of adoption, workflow disruption, changes in the patient-physician relationship, and costs and financial benefits are identified as relevant factors that influence the widespread use of RPM by healthcare providers; significant communication and other implementation preferences also emerged. Further research is needed to identify methods to address such concerns and use information collected in this study to develop protocols for RPM integration into clinical workflow

It is all about where you start: Text-to-image generation with seed selection

Text-to-image diffusion models can synthesize a large variety of concepts in new compositions and scenarios. However, they still struggle with generating uncommon concepts, rare unusual combinations, or structured concepts like hand palms. Their limitation is partly due to the long-tail nature of their training data: web-crawled data sets are strongly unbalanced, causing models to under-represent concepts from the tail of the distribution. Here we characterize the effect of unbalanced training data on text-to-image models and offer a remedy. We show that rare concepts can be correctly generated by carefully selecting suitable generation seeds in the noise space, a technique that we call SeedSelect. SeedSelect is efficient and does not require retraining the diffusion model. We evaluate the benefit of SeedSelect on a series of problems. First, in few-shot semantic data augmentation, where we generate semantically correct images for few-shot and long-tail benchmarks. We show classification improvement on all classes, both from the head and tail of the training data of diffusion models. We further evaluate SeedSelect on correcting images of hands, a well-known pitfall of current diffusion models, and show that it improves hand generation substantially

A Rare Case of Light Chain Amyloidosis of the Gastrointestinal Tract

A 65-year-old Hispanic female presented with a one-year history of anorexia, nausea, early satiety, epigastric discomfort, and a 20 kg weight loss. Computed tomography (CT) demonstrated heterogeneous liver parenchyma. Upper endoscopy revealed large, fungating, infiltrative mass at the lesser gastric curvature incisura, highly suspicious of gastric tumor; however, initial biopsy of the gastric mass was equivocal and an exploratory laparoscopy was performed. Repeated intraoperative biopsies of the gastric mass and of liver parenchyma demonstrated diffuse hyalinized stroma consistent with amyloid deposition, and a bone marrow biopsy confirmed the diagnosis of primary light chain (AL) amyloidosis