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Virtual patients using large language models: Scalable, contextualized simulation of clinician-patient dialog with feedback.
J Med Internet Res
January 2025
Division of General Internal Medicine, Mayo Clinic College of Medicine and Science, 200 First St SW, Rochester, US.
Background: Virtual patients (VPs) are computer screen-based simulations of patient-clinician encounters. VP use is limited by cost and low scalability.
Objective: Show proof-of-concept that VPs powered by large language models (LLMs) generate authentic dialogs, accurate representations of patient preferences, and personalized feedback on clinical performance; and explore LLMs for rating dialog and feedback quality.
Travel medicine training and experience among primary care physicians in Qatar.
PLoS One
January 2025
Department of Public Health and Preventive Medicine, Faculty of Medicine Cairo University, Cairo, Egypt.
Background: Travel medicine (TM) focuses on preventing and managing travel-related issues. Evidence has become more important than expert opinions in the development of TM standards. This study aimed to evaluate the training and experience of TM among Primary Care Physicians (PCPs) in Qatar and their associated factors.
View Article and Find Full Text PDFImpact of delirium on post-discharge mortality in coronary care unit patients: A retrospective cohort study.
Nurs Crit Care
January 2025
Department of Critical Care Medicine, Shenzhen Nanshan People's Hospital and the 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Background: Coronary care unit (CCU) patients surviving to discharge still face significant mortality. Delirium is common in CCU patients and has been associated with poorer CCU and in-hospital outcomes.
Aim: To assess the association between delirium and mortality after hospital discharge in CCU survivors.
Exploring timely and safe discharge from ICU: a comparative study of machine learning predictions and clinical practices.
Intensive Care Med Exp
January 2025
Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL, 32224, USA.
Background: The discharge practices from the intensive care unit exhibit heterogeneity and the recognition of eligible patients for discharge is often delayed. Recognizing the importance of safe discharge, which aims to minimize readmission and mortality, we developed a dynamic machine-learning model. The model aims to accurately identify patients ready for discharge, offering a comparison of its effectiveness with physician decisions in terms of safety and discrepancies in discharge readiness assessment.
View Article and Find Full Text PDFHigh PSMA Avidity, Yet Still Benign: The Importance of CT Morphology for Diagnosing Fibrous Dysplasia on PSMA-Targeted PET/CT.
Clin Nucl Med
January 2025
From the Molecular Imaging and Therapy, Hoag Family Cancer Institute, Irvine, CA.
A 64-year-old man with newly diagnosed prostate cancer underwent 18F-Piflufolastat PET/CT. Radiotracer avidity localized to the primary prostate malignancy and to a left rib (SUVmax, 9.0).
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