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http://dx.doi.org/10.1001/archoto.2012.1195 | DOI Listing |
Radiology
August 2025
Division of Intramural Research, National Library of Medicine, National Institutes of Health, Bethesda, Md.
Background With the growing use of multimodal large language models (LLMs), numerous vision-enabled models have been developed and made available to the public. Purpose To assess and quantify the advancements of multimodal LLMs in interpreting radiologic quiz cases by examining both image and textual content over the course of 1 year, and to compare model performance with that of radiologists. Materials and Methods For this retrospective study, 95 questions from Case of the Day at the RSNA 2024 Annual Meeting were collected.
View Article and Find Full Text PDFAm J Kidney Dis
August 2025
Department of Nephrology, Jawaharlal Institute of Medical Education and Research (JIPMER), Puducherry, India. Electronic address:
J Cancer Educ
May 2025
Department of Radiation Oncology, SUNY Upstate Medical University, Syracuse, NY, USA.
There is a need to teach interdisciplinary education in undergraduate medical education to encourage the fundamentals of teamwork and communication for enhanced patient outcomes. This report describes a novel interdisciplinary education session in the form of a simulated multidisciplinary oncology tumor board (TB) for pre-clinical medical students. Goals included the following: review of select pre-clinical lung cancer learning points, demonstration of diagnostic techniques relevant to the workup of lung cancer, and exposition of multidisciplinary and interprofessional teamwork.
View Article and Find Full Text PDFAm Surg
May 2025
Department of General Surgery, Bagcilar Training and Research, University of Health Sciences, Istanbul, Turkey.
ObjectiveThis study aimed to evaluate the performance of large language models (LLMs) in answering questions from the American Board of Surgery In-Training Examination (ABSITE).MethodsMultiple choice ABSITE Quiz was entered into the most popular LLMs as prompts. ChatGPT-4 (OpenAI), Copilot (Microsoft), and Gemini (Google) were used in the study.
View Article and Find Full Text PDFArthroscopy
April 2025
Department of Orthopedic Surgery, NYU Langone Orthopedic Hospital, New York, New York, U.S.A.
Purpose: To assess whether an artificial intelligence (AI) translation of a magnetic resonance imaging (MRI) report improved patient understanding of the information presented in the radiology report and to evaluate patient preferences for AI translations over traditional radiology reports.
Methods: Patients presenting to an orthopaedic surgery clinic were prospectively enrolled and randomized into 2 groups. The standard MRI group received a traditional MRI report on a multiligament knee injury written by a radiologist, whereas the AI group received an AI-translated version of the same report, generated using ChatGPT version 4.