INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems".
Anti-seizure medications (ASMs) are often prescribed using a trial-and-error approach with a similar sequence for many patients. Comparative effectiveness data beyond the first ASM prescription are limited. Artificial intelligence can automatically e...
High-quality segmentation is important for AI-driven radiological research and clinical practice, with the potential to play an even more prominent role in the future. As medical imaging advances, accurately segmenting anatomical and pathological str...
International journal of medical informatics
Oct 1, 2025
This critical review explores two interrelated trends: the rapid increase in studies on machine learning (ML) applications within health informatics and the growing concerns about the reproducibility of these applications across different healthcare ...
This article addresses how the dynamic, interdisciplinary, and non-linear nature of health information technology implementations require a workforce equipped with both technical competencies and an understanding of the relationships between healthca...
Ontologies are structured frameworks for representing knowledge by systematically defining concepts, categories, and their relationships. While widely adopted in biomedicine, ontologies remain largely absent in mental health research and clinical car...
Clinical informatics (CI) is an emerging field within biomedical informatics that sits at the intersection of clinical care, health systems, and health information technology (IT). CI emphasizes how individuals (neurologists, patients, staff) interac...
BACKGROUND: Recent advancements in general multimodal large language models (MLLMs) have led to substantial improvements in the performance of biomedical MLLMs across diverse medical tasks, exhibiting significant transformative potential. However, th...
OBJECTIVE: A large proportion of electronic health record (EHR) data consists of unstructured medical language text. The formatting of this text is often flexible and inconsistent, making it challenging to use for predictive modeling, clinical decisi...
OBJECTIVE: Interest has grown in combining radiology, pathology, genomic, and clinical data to improve the accuracy of diagnostic and prognostic predictions toward precision health. However, most existing works choose their datasets and modeling appr...
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