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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.

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.

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.

The use of real-world data (RWD) in artificial intelligence (AI) applications for healthcare offers unique opportunities but also poses complex challenges related to interpretability, transparency, safety, efficacy, bias, equity, privacy, ethics, accountability, and stakeholder engagement.

Patients of different race have different outcomes following renal transplantation. Patients of different race also undergo renal transplantation at different hospitals. We used a novel decentralized multisite approach to quantitatively assess the effect of site of care on racial disparities between non-Hispanic Black (NHB) and non-Hispanic White (NHW) patients in post-transplantation survival times.

Breast diffusion-weighted imaging (DWI) has shown potential as a standalone imaging technique for certain indications, eg, supplemental screening of women with dense breasts. This study evaluates an artificial intelligence (AI)-powered computer-aided diagnosis (CAD) system for clinical interpretation and workload reduction in breast DWI.

Automation of clinical orders in electronic health records (EHRs) has the potential to reduce clinician burden and enhance patient safety. However, determining which orders are appropriate for automation requires a structured framework to ensure clinical validity, transparency, and safety.

Rule-based structured data algorithms and natural language processing (NLP) approaches applied to unstructured clinical notes have limited accuracy and poor generalizability for identifying immunosuppression. Large language models (LLMs) may effectively identify patients with heterogenous types of immunosuppression from unstructured clinical notes. We compared the performance of LLMs applied to unstructured notes for identifying patients with immunosuppressive conditions or immunosuppressive medication use against 2 baselines: (1) structured data algorithms using diagnosis [...]

Building upon our previous work on predicting treatment retention in medications for opioid use disorder, we aimed to improve 6-month retention prediction in buprenorphine-naloxone (BUP-NAL) therapy by incorporating features derived from large language models (LLMs) applied to unstructured clinical notes.

Text embeddings are promising for semantic tasks, such as retrieval augmented generation (RAG). However, their application in health care is underexplored due to a lack of benchmarking methods. We introduce a scalable benchmarking method to test embeddings for health-care semantic tasks.