Physician vs. AI-generated messages in urology: evaluation of accuracy, completeness, and preference by patients and physicians.

Purpose: To evaluate the accuracy, comprehensiveness, empathetic tone, and patient preference for AI and urologist responses to patient messages concerning common BPH questions across phases of care.

Methods: Cross-sectional study evaluating responses to 20 BPH-related questions generated by 2 AI chatbots and 4 urologists in a simulated clinical messaging environment without direct patient interaction. Accuracy, completeness, and empathetic tone of responses assessed by experts using Likert scales, and preferences and perceptions of authorship (chatbot vs.

Utilizing eye tracking to assess electronic health record use by pharmacists in the intensive care unit.

Purpose: A study was conducted using high-fidelity electronic health record (EHR)-based simulations with incorporated eye tracking to understand the workflow of critical care pharmacists within the EHR, with specific attention to the data elements most frequently viewed.

Methods: Eight critical care pharmacists were given 25 minutes to review 3 simulated intensive care unit (ICU) charts deployed in the simulation instance of the EHR. Using monitor-based eye trackers, time spent reviewing screens, clinical information accessed, and screens used to access specific information were reviewed and quantified to look for trends.

Pilot Testing of Simulation in the Evaluation of a Novel, Rapidly Deployable Electronic Health Record for use in Disaster Intensive Care.

Objectives: The SARS-CoV-2 pandemic has highlighted the need for rapid creation and management of ICU field hospitals with effective remote monitoring which is dependent on the rapid deployment and integration of an Electronic Health Record (EHR). We describe the use of simulation to evaluate a rapidly scalable hub-and-spoke model for EHR deployment and monitoring using asynchronous training.

Methods: We adapted existing commercial EHR products to serve as the point of entry from a simulated hospital and a separate system for tele-ICU support and monitoring of the interfaced data.

Automated digital TIL analysis (ADTA) adds prognostic value to standard assessment of depth and ulceration in primary melanoma.

Accurate prognostic biomarkers in early-stage melanoma are urgently needed to stratify patients for clinical trials of adjuvant therapy. We applied a previously developed open source deep learning algorithm to detect tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) images of early-stage melanomas. We tested whether automated digital (TIL) analysis (ADTA) improved accuracy of prediction of disease specific survival (DSS) based on current pathology standards.

Deep Learning Based on Standard H&E Images of Primary Melanoma Tumors Identifies Patients at Risk for Visceral Recurrence and Death.

Purpose: Biomarkers for disease-specific survival (DSS) in early-stage melanoma are needed to select patients for adjuvant immunotherapy and accelerate clinical trial design. We present a pathology-based computational method using a deep neural network architecture for DSS prediction.

Experimental Design: The model was trained on 108 patients from four institutions and tested on 104 patients from Yale School of Medicine (YSM, New Haven, CT).

A deep learning approach for real-time detection of sleep spindles.

Objective: Sleep spindles have been implicated in memory consolidation and synaptic plasticity during NREM sleep. Detection accuracy and latency in automatic spindle detection are critical for real-time applications.

Approach: Here we propose a novel deep learning strategy (SpindleNet) to detect sleep spindles based on a single EEG channel.