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To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions.

To evaluate predictive diagnostic performance of open- and closed-source large language models (LLMs) in emergency medicine, addressing the urgent need for innovative clinical decision support tools amid rising patient volumes and staffing shortages.

The Bridge2AI program is establishing rules of practice for creating ethically sourced health data repositories to support the effective use of ML/AI in biomedical and behavioral research. Given the initially undefined nature of ethically sourced data, this work concurrently developed definitions and guidelines alongside repository creation, grounded in a practical, operational framework.

To develop and evaluate a patient-centric medication module within a personal health record (PHR) app for capturing medication use, focusing on accuracy, usability, and concordance.

To develop ECG-FM, an open-weight foundation model for electrocardiogram (ECG) analysis, rigorously evaluate its performance on clinically salient tasks, and openly release it alongside a public benchmark.

Severe maternal morbidity and mortality are higher in the United States than in other high-income countries, and unacceptable disparities persist. To facilitate research on these outcomes, we developed a standardized approach for extracting perinatal data from electronic health records (EHRs).

Joint recognition and ICD-10 linking of diagnoses in bilingual, non-standard Spanish and Catalan primary care notes is challenging. We evaluate parameter-efficient fine-tuning (PEFT) techniques as a resource-conscious alternative to full fine-tuning (FFT) for multi-label clinical text classification.

Despite the rapid development of AI in clinical medicine, reproducibility and methodological limitations hinder its clinical utility. In response, MINimum Information for Medical AI Reporting (MINIMAR) standards were introduced to enhance publication standards and reduce bias, but their application remains unexplored. In this review, we sought to assesses the quality of reporting in AI/ML studies of cardiac amyloidosis (CA) an increasingly important cause of heart failure.

This study evaluates response rates of pharmacogenomics (PGx) nonsteroidal anti-inflammatory drugs (NSAIDs) clinical decision support (CDS) alerts at Sanford Health from May 2020 to December 2024.

Accelerate adoption of clinical research technology that obtains electronic health record (EHR) data through HL7 Fast Healthcare Interoperability Resources (FHIR).