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Enhancing Medical Student Engagement Through Cinematic Clinical Narratives: Multimodal Generative AI-Based Mixed Methods Study.
JMIR Med Educ
January 2025
Department of Medical Education, University of Idaho, 875 Perimeter Drive MS 4061, WWAMI Medical Education, Moscow, ID, 83844-9803, United States, 1 5092090908.
Background: Medical students often struggle to engage with and retain complex pharmacology topics during their preclinical education. Traditional teaching methods can lead to passive learning and poor long-term retention of critical concepts.
Objective: This study aims to enhance the teaching of clinical pharmacology in medical school by using a multimodal generative artificial intelligence (genAI) approach to create compelling, cinematic clinical narratives (CCNs).
Structural and load-dependent arterial stiffness across the adult life span.
J Hypertens
December 2024
Department of Ultrasound Medicine, Tangdu Hospital, Air Force Medical University.
Background: The arterial stiffening is attributed to the intrinsic structural stiffening and/or load-dependent stiffening by increased blood pressure (BP). The respective lifetime alterations and major determinants of the two components with normal aging are not clear.
Methods: A total of 3053 healthy adults (1922 women) aged 18-79 years were enrolled.
Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.
J Alzheimers Dis
January 2025
Department of Neurology and the Franke Barrow Global Neuroscience Education Center, Barrow Neurological Institute, Phoenix, AZ, USA.
Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.
Objective: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults.
Methods: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.
CLEFT: Language-Image Contrastive Learning with Efficient Large Language Model and Prompt Fine-Tuning.
Med Image Comput Comput Assist Interv
October 2024
Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
Recent advancements in Contrastive Language-Image Pre-training (CLIP) [21] have demonstrated notable success in self-supervised representation learning across various tasks. However, the existing CLIP-like approaches often demand extensive GPU resources and prolonged training times due to the considerable size of the model and dataset, making them poor for medical applications, in which large datasets are not always common. Meanwhile, the language model prompts are mainly manually derived from labels tied to images, potentially overlooking the richness of information within training samples.
View Article and Find Full Text PDFPrimary splenic diffuse large B-cell lymphoma presenting with a gastrosplenic fistula: Case report.
Radiol Case Rep
March 2025
Department of Radiology, University of Missouri, Columbia, MO, USA.
Primary splenic diffuse large B-cell lymphoma (PS-DLBCL) is a rare manifestation of malignant lymphoma. Although DLBCL is the most common subtype of non-Hodgkin lymphoma, primary splenic involvement is uncommon. Additionally, a gastrosplenic fistula at initial presentation is even more rare and poses a diagnostic challenge for the radiologist.
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