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The objective of this study was to develop and test natural language processing (NLP) methods for screening and, ultimately, predicting the cancer relevance of peer-reviewed publications.

To develop and evaluate an automated classification system for labeling Exposure Process Coding System (EPCS) quality codes-specifically exposure and encourage events-during in-person exposure therapy sessions using automatic speech recognition (ASR) and natural language processing techniques.

Neonatal jaundice monitoring is resource-intensive. Existing artificial intelligence methods use image or clinical data, but none systematically combine both or compare feature contributions. This study fills that gap by extracting and analyzing multimodal features on a large dataset, identifying an optimal feature set for accurate, accessible jaundice assessment.

Prediction models using statistical or machine learning (ML) approaches can enhance clinical decision support tools. Infliximab (IFX), a biologic with a newly introduced biosimilar for Crohn's disease (CD) and ulcerative colitis (UC), presents an opportunity to evaluate these tools at time of biosimilar switch to predict disease flares. This study sought to evaluate real-world safety and effectiveness of nonmedical IFX biosimilar switch in a national US cohort of CD and [...]

To use more precise measures of which hospitals are electronically connected to determine whether health information exchange (HIE) is associated with lower emergency department (ED)-related utilization.

The number of ethical frameworks designed to guide artificial intelligence (AI) use has grown substantially over the past decade, yet their real-world effect remains unclear. We aimed to synthesize existing evidence to analyze the practical impact of AI ethics frameworks (AIEFs) operationalized in healthcare.

To understand whether patients prefer chatbots for certain tasks in healthcare, and their motivations for doing so, recognizing that chatbots are already assisting patients with various healthcare tasks.

This perspective explores how ambient artificial intelligence (AI) scribes could support documentation and quality improvement (QI) of structured, team-based provider-to-provider communication in acute care settings.