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The integration of artificial intelligence (AI) into health care is critically dependent on vast quantities of patient data, igniting an urgent global debate on data ownership, privacy, and governance. While numerous perspectives exist, the empirical structure and evolution of this scholarly discourse remain uncharacterized. We aimed to systematically map the conceptual landscape of research on AI and health data governance to identify its core themes, temporal trends, and key focus [...]

Early warning systems (EWSs) help clinicians identify deteriorating patients using clinical data, such as vital signs. However, standard systems struggle to capture nuanced nursing concerns. The Healthcare Process Model-ExpertSignals (HPM-ExpertSignals) framework describes how nurses' concerns are reflected in their documentation patterns. While a recent trial showed positive outcomes, the predictive gain of combining both data types remains unquantified.

With the rapid growth of unstructured clinical narratives in electronic health records (EHRs), clinical named entity recognition (NER) has become a crucial technique for extracting structured medical information. However, traditional supervised models such as CRF and BioClinicalBERT rely on costly manual annotations. Although large language model (LLM)-based zero-shot NER reduces the dependency on labeled data, challenges remain in aligning example selection with task granularity and in effectively integrating prompt design [...]

Characterise longitudinal patterns of chiropractic visits for neck pain or low back pain by using machine learning (ML) methods and explainable models.

The "Projections In Multiple Sclerosis" (PRIMUS) project aims to develop a precision medicine platform enabling neurologists to support therapeutic decisions in multiple sclerosis by visualizing similar patient data in a reference database. We present a data integration method to combine randomized clinical trials (RCTs) and observational studies data and optimize the informativeness of the resulting database.

This study aims to develop and evaluate a trustworthy and ethical-by-design machine learning (ML) framework for predicting 5-year cancer survival across 10 Surveillance, Epidemiology, and End Results (SEER) cancer types. We assessed ML model(s) performance across localized, regional, and distant stages while examining ML fairness, ML explainability, and the added value of social determinants of health (SDOH) as features. The goal is to advance clinically interpretable and equity-centered survival prediction [...]

To develop a large-language-model (LLM)-centric workflow flow extraction and migration of clinician-documented colonoscopy recall recommendations from unstructured reports and letters during an enterprise-wide electronic health record (EHR) transition.

Electronic Health Record (EHR) data are increasingly used in cancer research, yet the fidelity of this data when exchanged between systems remains poorly quantified. This study investigated the agreement in essential biomarker data after they are passed from the EHR into the cancer registry and Fast Healthcare Interoperability Resources (FHIR) extracts.