Graph theoretic visualization of patient and health worker messaging in the EHR.

Introduction: The electronic health record (EHR) has greatly expanded healthcare communication between patients and health workers. However, the volume and complexity of EHR messages have increased health workers' cognitive load, impeding effective care delivery and contributing to burnout.

Methods: To understand these potential detriments resulting from EHR communication, we analyzed EHR messages sent between patients and health workers at Emory Healthcare, a large academic healthcare system in Atlanta, Georgia.

The association of azole antifungals with overall survival in patients with non-small cell lung cancer receiving immune checkpoint inhibitors.

Background: Preclinical data suggest antifungal azole derivatives have antitumor efficacy that may modulate response to immune checkpoint inhibitors (ICIs). We aimed to evaluate the association of azole drugs with overall survival (OS) in a population of patients with non-small cell lung cancer (NSCLC) treated with ICI within the Veterans Health Administration (VHA).

Methods: In this retrospective study, the VA Corporate Data Warehouse was queried for patients diagnosed with NSCLC and treated with ICI from 2010 to 2018.

Summary of the National Cancer Institute 2023 Virtual Workshop on Medical Image De-identification-Part 2: Pathology Whole Slide Image De-identification, De-facing, the Role of AI in Image De-identification, and the NCI MIDI Datasets and Pipeline.

De-identification of medical images intended for research is a core requirement for data sharing initiatives, particularly as the demand for data for artificial intelligence (AI) applications grows. The Center for Biomedical Informatics and Information Technology (CBIIT) of the United States National Cancer Institute (NCI) convened a two half-day virtual workshop with the intent of summarizing the state of the art in de-identification technology and processes and exploring interesting aspects of the subject. This paper summarizes the highlights of the second day of the workshop, the recordings and presentations of which are publicly available for review.

Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection.

Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of overfitting, where small training data sets can result in unreliable outcomes. The second challenge is the need for more generalizability, the lack of which creates difficulties in implementing the technology across various institutions and practices.