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To evaluate whether retrieval-augmented generation (RAG) can serve as an efficient alternative to long-context prompting for clinical reasoning over electronic health records (EHRs).

Artificial intelligence (AI) is increasingly being used in healthcare settings, yet evidence of its real-world value remains inconsistent. Current evaluation paradigms often emphasize methodological rigor and technical validity over measurable improvements in patient outcomes or system performance.

Artificial intelligence (AI) is increasingly being deployed in health-care settings, yet health systems remain at an early stage in developing approaches to scale effective applications. Much of current activity is characterized by early-stage use cases and pilot projects. In this study, we explored how different health systems are approaching the development, adoption, implementation, and scaling of AI in healthcare to identify transferable lessons for system-level change.

To systematically examine different clinical data modalities in large language models (LLMs) and multimodal large language models (MLLMs), and to quantify the contribution of data modalities in early inpatient risk prediction and decision support tasks.

Human Factors (HF) principles are essential for safe and effective clinical decision support (CDS), yet existing guidance is fragmented and rarely evaluated in real world settings. A novel, evidence-based, vendor-agnostic HF-informed guideline was developed to address this gap. This study evaluated its perceived usefulness, usability, and impact on CDS design and optimization.

Clinical AI systems and models increasingly need traceable documentation that makes intended use, performance evidence, transparency, and lifecycle status explicit, yet existing model cards, datasheets, reporting guidelines, and other documentation approaches do not consistently bring together structured clinical fields, vocabulary-linked cohort descriptions, structured performance reporting, and governed documentation history. We introduce Structured, Meaningful, Auditable, Responsible, and Transparent (SMART), a framework that adapts the model card concept into structured, lifecycle-aware documentation [...]

Information extraction (IE) from clinical texts has advanced rapidly with recent advances in natural language processing, particularly the advent of large language models (LLMs). However, inconsistent and incomplete reporting of methodologies limits reproducibility, comparability, and clinical translation. We aimed to develop a consensus-based reporting guideline tailored to clinical IE studies.

This study aimed to identify trajectories of multimorbidity following acute myocardial infarction (AMI), using explainable temporal machine-learning methods, and assess their clinical, prognostic, and biological significance.

The potential of "big data" in health research remains largely untapped, particularly concerning real-world data sources such as administrative health data and electronic medical records. While healthcare insurance claims data have been essential for assessing medication safety and effectiveness within indicated patient populations, exploring broader drug-outcome associations could uncover significant insights.