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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.

The COVID-19 pandemic accelerated the adoption of digital health technologies, including telehealth, electronic medical records, and patient-facing applications. These tools maintained access to care during periods of restriction but exposed gaps in workforce readiness, patient trust, digital literacy, and equity. Despite widespread implementation, limited evidence exists on how patients and healthcare providers experienced this transition and how these experiences influenced digital health adoption.

Large language models are rapidly transforming medical education, yet their performance in Allergy/Immunology remains insufficiently characterized. Furthermore, concerns regarding accuracy, consistency, and sensitivity to input format persist.

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.

Evaluation of ambient AI on patient experience, documentation efficiency, clinician workload, and clinical throughput across a large emergency department (ED) network.