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To develop a locally deployed DeepSeek-powered closed-loop system for electronic nursing documentation quality control (QC) and evaluate its clinical efficacy through a multidimensional validation framework.

This study aims to compare the diagnostic abilities of humans in wound image assessment with those of an AI-based model, examine how "expertise" affects clinicians' diagnostic performance, and investigate the heterogeneity in clinical judgments.

This review paper comprehensively summarizes healthcare provider (HCP) evaluation of explanations produced by explainable artificial intelligence methods to support point-of-care, patient-specific, clinical decision-making (CDM) within medical settings. It highlights the critical need to incorporate human-centered (HCP) evaluation approaches based on their CDM needs, processes, and goals.

This paper presents the results from a competition challenging participants to develop entity linking models using a subset of annotated MIMIC-IV-Note data and the SNOMED CT Terminology.

To explore the potential effects of recent restrictions on discussions regarding diversity, equity, and inclusion (DEI) in the field of biomedical informatics.

Evaluate the association between telemedicine intensity and ambulatory physician electronic health record (EHR) use following the COVID-19 pandemic.

To characterize and demonstrate how to reduce the administrative burden experienced by patients when navigating medication affordability resources in the United States.

Machine learning algorithms can advance clinical care, including identifying mental health conditions. These algorithms are often developed without considering the perspectives of the affected populations. This study describes the process of incorporating end-user perspectives into the development and implementation planning of a prediction algorithm for new perinatal depression onset.

To determine how much of the current biomedical literature would be flagged or require modification in relation to the presence of terms from leaked lists prepared by the Centers for Disease Control (CDC), the National Science Foundation (NSF), and the National Security Administration (NSA) in early 2025.

To conduct a scoping review (ScR) of existing approaches for synthetic Electronic Health Records (EHR) data generation, to benchmark major methods, and to provide an open-source software and offer recommendations for practitioners.