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Elevated blood pressure (BP) and hypertension are often overlooked in pediatric care. We adapted a pediatric hypertension clinical decision support (CDS) for a primarily rural health system and compared CDS impact across varied implementation approaches.

We aimed to identify and map recent studies using high-frequency, time-series electronic patient-generated health data (ePGHD) to assess treatment response; characterize ePGHD types and collection methods; summarize ePGHD-based definitions of treatment response; and describe analytical approaches used.

Automated literature screening in biomedical research is often hindered by domain shifts and scarcity of labeled data, which limit model accuracy and generalizability. While large language models (LLMs) perform well in zero-shot settings, they often fail to capture complex, domain-specific reasoning patterns. To address this limitation, this study investigates whether an interactive, weakly supervised learning framework combining GPT (generative pre-trained transformer)'s fine-tuning adaptability with DeepSeek's reasoning capabilities can improve literature [...]

Skin cancer is the most common malignancy in the United States, with more than five million cases diagnosed annually among 3.3 million individuals. Melanoma, the deadliest form of skin cancer, accounts for roughly 200 000 new diagnoses each year and nearly 10 000 deaths. AI-based skin cancer detection is being developed and tested in laboratory and academic settings as a promising approach to improve access and reduce disparities. However, current models often [...]

We conducted the Clinical Registry Extraction and Data Submission (CREDS) project to evaluate the readiness of HL7 Fast Healthcare Interoperability Resources (FHIR) for provisioning data from health information systems for the American College of Cardiology Cardiac Catheterization Percutaneous Coronary Intervention (CathPCI) Registry.

In 2025, the National Academy of Medicine released an Artificial Intelligence Code of Conduct (AICC). In this commentary, we examine how the AICC introduces governance mechanisms to oversee AI applications and how it can support the ethical development and responsible use of AI in healthcare, paying special attention to the role of nurses.

This study aims to compare the effectiveness of 2 ambient AI scribe technologies in reducing physician burnout, improving workflow satisfaction, and enhancing documentation efficiency through a randomized crossover trial.

To develop and evaluate a human-LLM (Large Language Model) collaborative approach for systematic ontology updating, demonstrated with the Dietary Lifestyle Ontology (DILON).

Research on artificial intelligence (AI)-based clinical decision-support (AI-CDS) systems has returned mixed results. Sometimes providing AI-CDS to a clinician will improve decision-making performance, sometimes it will not, and it is not always clear why. This scoping review seeks to clarify existing evidence by identifying clinician-level and technology design factors that impact the effectiveness of AI-assisted decision-making in medicine.