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Evaluation of an image-rich quiz-based iOS app as a study resource for the ABR Core exam.
Curr Probl Diagn Radiol
January 2025
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address:
The American Board of Radiology Core exam requires that trainees demonstrate knowledge of critical concepts across 12 domains spanning a range of imaging modalities and anatomic regions. Mobile apps have become popular components of medical and radiology education since the inception of smartphones. Numerous medical educational apps are accessible via smartphone devices and tablets, regardless of operating system, for medical training and learning purposes.
View Article and Find Full Text PDFStructured clinical reasoning prompt enhances LLM's diagnostic capabilities in diagnosis please quiz cases.
Jpn J Radiol
December 2024
Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 113-8655, Japan.
Purpose: Large Language Models (LLMs) show promise in medical diagnosis, but their performance varies with prompting. Recent studies suggest that modifying prompts may enhance diagnostic capabilities. This study aimed to test whether a prompting approach that aligns with general clinical reasoning methodology-specifically, using a standardized template to first organize clinical information into predefined categories (patient information, history, symptoms, examinations, etc.
View Article and Find Full Text PDF"This Is a Quiz" Premise Input: A Key to Unlocking Higher Diagnostic Accuracy in Large Language Models.
Cureus
October 2024
Radiology, The University of Tokyo, Tokyo, JPN.
Purpose Large language models (LLMs) are neural network models that are trained on large amounts of textual data, showing promising performance in various fields. In radiology, studies have demonstrated the strong performance of LLMs in diagnostic imaging quiz cases. However, the inherent differences in prior probabilities of a final diagnosis between clinical and quiz cases pose challenges for LLMs, as LLMs had not been informed about the quiz nature in previous literature, while human physicians can optimize the diagnosis, consciously or unconsciously, depending on the situation.
View Article and Find Full Text PDFInfluence of Teaching Satisfaction of Search Interpretation Errors on Detection of Radiographic Edge-and-Corner Lesions by Fourth-Year Veterinary Students.
J Vet Med Educ
August 2024
Department of Clinical Sciences, Kansas State College of Veterinary Medicine, 1800 Denison Ave. Manhattan, KS 66502 USA.
Edge-and-corner (E&C) pathology is defined as clinically relevant findings in diagnostic imaging that are located at the physical periphery of studies and thus easily overlooked. Satisfaction of search is a perceptive interpretation error which can compound the difficulty of detecting E&C lesions. Guiding veterinary students to systematically identify these lesions would likely benefit their training, and the authors sought to determine whether teaching the concept of satisfaction of search could influence students' ability to detect E&C lesions.
View Article and Find Full Text PDFImplementation of an Optimized Preoperative Checklist for Endoscopic Sinus Surgery Within a Multiinstitutional Resident Education Curriculum.
Am J Rhinol Allergy
January 2025
Department of Otolaryngology-Head & Neck Surgery, Columbia University Irving Medical Center, New York, New York.
Background: Preoperative review of computed tomography (CT) imaging assists with endoscopic sinus surgery (ESS) planning, where trainees may benefit from a systematic approach. We have previously developed an optimized preoperative checklist for sinus CT imaging using an iterative modified Delphi method.
Objective: In this study, we assess the utility of an optimized preoperative checklist for residents performing ESS.
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