JMIR Medical Informatics

Lateral malleolar avulsion fractures (LMAFs) and subfibular ossicles (SFOs) are distinct entities that both present as small bone fragments near the lateral malleolus in imaging but require different treatment strategies. Clinical and radiological differentiation is challenging, which can impede timely and precise management. Magnetic resonance imaging (MRI) is the diagnostic gold standard for differentiating LMAFs from SFOs, whereas radiological differentiation using computed tomography (CT) alone is challenging in routine practice. Deep convolutional neural networks (DCNNs) have shown promise in musculoskeletal imaging diagnostics, but robust, multicenter evidence in this specific context is lacking.

Drug-target interaction prediction is crucial in drug repositioning, as it can significantly reduce research and development costs and shorten the development cycle. Most existing deep learning-based approaches employ graph neural networks for drug-target interaction prediction. However, these approaches still face limitations in capturing complex biochemical features, integrating multi-level information, and providing interpretable model insights.

Natural language processing (NLP) techniques are useful to identify stigmatizing language in electronic health records (EHRs) but require careful consideration. This commentary article builds on “Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning” by Chen et al., which highlights the importance of incorporating situational and temporal contexts in annotation and modeling efforts. We emphasize the need for researchers to explicitly articulate their paradigms and positionality, particularly when working with populations disproportionately affected by stigmatizing language. We also explore the differences arising from conflicting preferences across communities about what constitutes destigmatizing language. We discuss participatory and trust-centered approaches for model development to work towards unbiased impact. Such strategies have a crucial role in raising awareness and fostering inclusive healthcare.

Planning for coronary artery bypass grafting (CABG) necessitates advanced spatial visualization skills and consideration of multiple factors, including the depth of coronary arteries within the subepicardium, calcification levels, and pericardial adhesions.

The integration of digital tools into psychotherapy has gained increasing attention, particularly for practices such as Routine Outcome Monitoring (ROM), which involve the regular collection of patient-reported data to inform treatment decisions. However, despite the potential benefits, the adoption of digital platforms remains limited, partly due to usability concerns and workflow misalignment.

Asthma is a common chronic respiratory disease with increasing prevalence among children over the past few decades. It can cause significant respiratory symptoms and acute exacerbations, often requiring emergency care or hospitalization. Moreover, exposure to respiratory viral infections, such as COVID-19 and influenza, can trigger severe complications in children with asthma. Despite these concerns, few studies have directly compared the in-hospital outcomes of children with asthma experiencing these infections.

Chronic diseases present significant challenges in healthcare, requiring effective management to reduce morbidity and mortality. While digital technologies like wearable devices and mobile applications have been widely adopted, Large Language Models (LLMs) such as ChatGPT are emerging as promising technologies with the potential to enhance chronic disease management. However, the scope of their current applications in chronic disease management and associated challenges remains underexplored

The shortage of pediatric medical resources and overcrowding in children’s hospital are severe issues in China. Accurately predicting waiting times can help optimize hospital operational efficiency.

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Data scarcity and dispersion pose significant obstacles in biomedical research, particularly when addressing rare diseases. In such scenarios, synthetic data generation (SDG) has emerged as a promising path to mitigate the first issue. Concurrently, federated learning is a machine learning paradigm where multiple nodes collaborate to create a centralized model with knowledge that is distilled from the data in different nodes, but without the need for sharing it. This research explores the combination of SDG and federated learning technologies in the context of acute myeloid leukemia, a rare hematological disorder, evaluating their combined impact and the quality of the generated artificial datasets.

Pathology reports contain critical information necessary to manage cancer patient care. Efforts to structure pathology cancer reports by the College of American Pathologists and the International Collaboration on Cancer Reporting (ICCR) have been successful in standardizing pathology reports. Likewise, methods to improve data computability and exchange by standards development organizations have progressed to make pathology cancer reports interoperable.