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Using Large Language Models to Detect and Understand Drug Discontinuation Events in Web-Based Forums: Development and Validation Study.
J Med Internet Res
January 2025
Department of Industrial and Systems Engineering, The University of Florida, GAINESVILLE, FL, United States.
Background: The implementation of large language models (LLMs), such as BART (Bidirectional and Auto-Regressive Transformers) and GPT-4, has revolutionized the extraction of insights from unstructured text. These advancements have expanded into health care, allowing analysis of social media for public health insights. However, the detection of drug discontinuation events (DDEs) remains underexplored.
View Article and Find Full Text PDFAccuracy of remote, video-based supraventricular tachycardia detection in patients undergoing elective electrical cardioversion: a prospective cohort.
J Clin Monit Comput
January 2025
Department of Electrical Engineering, Eindhoven University of Technology, Groene Loper 3, 5612 AZ, Eindhoven, the Netherlands.
Unobtrusive pulse rate monitoring by continuous video recording, based on remote photoplethysmography (rPPG), might enable early detection of perioperative arrhythmias in general ward patients. However, the accuracy of an rPPG-based machine learning model to monitor the pulse rate during sinus rhythm and arrhythmias is unknown. We conducted a prospective, observational diagnostic study in a cohort with a high prevalence of arrhythmias (patients undergoing elective electrical cardioversion).
View Article and Find Full Text PDFPreliminary exploration of ChatGPT-4 shows the potential of generative artificial intelligence for culturally tailored, multilingual antimicrobial resistance awareness messaging.
Am J Vet Res
January 2025
Center for Animal Health and Food Safety, Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, Saint Paul, MN.
Objective: Antimicrobial resistance (AMR), a global threat driven by factors such as improper antimicrobial use in humans and animals, is projected to cause 10 million annual deaths by 2050. For behavior change, public health messages must be tailored for diverse audiences. Generative AI may have the potential to create culturally and linguistically suited AMR awareness messages.
View Article and Find Full Text PDFAccuracy of latest large language models in answering multiple choice questions in dentistry: A comparative study.
PLoS One
January 2025
Faculty of Dentistry, PHENIKAA University, Hanoi, Vietnam.
Objectives: This study aims to evaluate the performance of the latest large language models (LLMs) in answering dental multiple choice questions (MCQs), including both text-based and image-based questions.
Material And Methods: A total of 1490 MCQs from two board review books for the United States National Board Dental Examination were selected. This study evaluated six of the latest LLMs as of August 2024, including ChatGPT 4.
Use of ChatGPT Large Language Models to Extract Details of Recommendations for Additional Imaging From Free-Text Impressions of Radiology Reports.
AJR Am J Roentgenol
January 2025
Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Boston, MA 02120 Phone: 617-525-9702.
Automated extraction of actionable details of recommendations for additional imaging (RAIs) from radiology reports could facilitate tracking and timely completion of clinically necessary RAIs and thereby potentially reduce diagnostic delays. To assess the performance of large-language models (LLMs) in extracting actionable details of RAIs from radiology reports. This retrospective single-center study evaluated reports of diagnostic radiology examinations performed across modalities and care settings within five subspecialties (abdominal imaging, musculoskeletal imaging, neuroradiology, nuclear medicine, thoracic imaging) in August 2023.
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