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To evaluate large language models (LLMs) for extracting temporal relations from pediatric rare disease clinical reports to enable automated patient timeline creation.

To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

This study evaluates and enhances language access services for Limited English Proficiency (LEP) patients in a large urban health system by integrating interpreter services into the Electronic Health Record (EHR), aiming to reduce care disparities and improve the digital experience for both patients and clinicians.

In a sample of patients with diagnosed alcohol use disorder (AUD), we examined the prevalence of both stigmatizing (eg, "alcohol abuse") and highly stigmatizing, non-medical descriptors (eg, "alcoholic") documented in electronic health records (EHRs). We also assessed whether the use of these terms-entered by clinicians when documenting AUD using search tools and picklists-varied across patient subgroups within a regional health system.

To present the Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform-unified access to diverse clinical datasets from the Mount Sinai Health System (MSHS), along with computational infrastructure for AI-driven research and demonstrate its utility with 3 research projects.

To evaluate the transcription accuracy of 6 German-capable speech-to-text (STT) systems in simulated emergency medical services (EMS) environments, focusing on clinically relevant performance under noisy and multilingual field conditions.

To evaluate large language models (LLMs) for automating the assessment of clinician adherence to ESC heart failure pharmacotherapy guidelines.

Significant opportunities for understanding the co-occurrence of conditions across species in coincident households remain untapped. We determined the feasibility of creating a Companion Care Registry (CCR) for analysis of health data from the University of Colorado Health (UCHealth) patients and their companion animals who received veterinary care at the geographically-adjacent Colorado State University Veterinary Health System (CSU-VHS).

Different natural language processing (NLP) techniques have demonstrated promising results for data extraction from radiological reports. Both traditional rule-based methods like regular expressions (Regex) and modern large language models (LLMs) can extract structured information. However, comparison between these approaches for extraction of specific radiological data elements has not been widely conducted.