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To assess the capacity of a bespoke artificial intelligence (AI) process to help medical writers efficiently generate quality plain language summary abstracts (PLSAs).

To determine if natural language processing (NLP) and machine learning (ML) techniques accurately identify interview-based psychological stress and meaning/purpose data in child/adolescent cancer survivors.

Degenerative rotator cuff tears (DCTs) are the leading cause of shoulder pain, affecting 30%-50% of individuals over 50. Current phenotyping strategies for DCT use heterogeneous combinations of procedural and diagnostic codes and are concerning for misclassification. The objective of this study was to create standardized phenotypic algorithms to classify DCT status across electronic health record (EHR) systems.

Despite the recent adoption of large language models (LLMs) for biomedical information extraction (IE), challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed LLM-IE: a Python package for building complete IE pipelines.

Large language models (LLMs) are increasingly utilized in healthcare, transforming medical practice through advanced language processing capabilities. However, the evaluation of LLMs predominantly relies on human qualitative assessment, which is time-consuming, resource-intensive, and may be subject to variability and bias. There is a pressing need for quantitative metrics to enable scalable, objective, and efficient evaluation.

Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalous.

To describe the prevalence of and trends in practices that interfere with the exchange of patient health information (potential information blocking) 2 years after implementation of information-blocking regulations.

To develop a distributed algorithm to fit multi-center Cox regression models with time-varying coefficients to facilitate privacy-preserving data integration across multiple health systems.

Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.

The objectives of this study are to synthesize findings from recent research of retrieval-augmented generation (RAG) and large language models (LLMs) in biomedicine and provide clinical development guidelines to improve effectiveness.