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This quality improvement study implemented and prospectively examined user engagement with an artificial intelligence (AI)-powered clinical trial knowledge management application at an NCI-designated comprehensive cancer center.

Social and behavioral determinants of health (SBDH) are increasingly recognized as essential for prognostication and informing targeted interventions. Clinical notes often contain details about SBDH in unstructured format. Conventional extraction methods for these data tend to be labor intensive, inaccurate, and/or unscalable. In this study, we aim to develop and validate a large language model (LLM)-powered method to extract structured SBDH data from clinical notes through prompt engineering.

Emerging efforts to identify patients at risk of suicide have focused on the development of predictive algorithms for use in healthcare settings. We address a major challenge in effective risk modeling in healthcare settings with insufficient data with which to create and apply risk models. This study aimed to improve risk prediction using transfer learning or data fusion by incorporating risk information from external data sources to augment the data [...]

The success of artificial intelligence (AI) and machine learning (ML) approaches in biomedical research depends on the quality of the underlying data. The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Data Centric Challenge was designed to address the challenge of making raw clinical research data AI ready, with a focus on type 1 diabetes studies available in the NIDDK Central Repository (NIDDK-CR). This paper aims to present [...]

To assess the influence of an implemented artificial intelligence model predicting pediatric sepsis (defined by IPSO-Improving Pediatric Sepsis Outcomes collaborative) in the emergency department (ED) on human performance measures.

To conduct an independent external validation of an implemented vendor-developed emergency department (ED) pediatric sepsis predictive model.

This work incorporates the Digital Imaging Communications in Medicine (DICOM) Standard into the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) to standardize and accurately represent imaging studies, such as acquisition parameters, in multimodal research studies.

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.

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

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.