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Structured 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 PDFEnhancing Imaging Anatomy Competency: Integrating Digital Imaging and Communications in Medicine (DICOM) Viewers Into the Anatomy Lab Experience.
Cureus
September 2024
Anatomical Sciences, Edward Via College of Osteopathic Medicine, Auburn, USA.
Article Synopsis
- The study examines the impact of early exposure to Digital Imaging and Communications in Medicine (DICOM) on medical students' ability to identify anatomical structures.
- It compares performance between a test group of first-year students who received DICOM training and a control group of second-year students without such training.
- Results show the test group scored significantly better on quizzes, indicating that early introduction to DICOM can enhance learning outcomes in radiologic interpretation.
Instagram reels versus image posts in radiology education.
Curr Probl Diagn Radiol
August 2024
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 601 North Caroline Street, JHOC 3254, Baltimore, MD 21287, USA.
Objective: In January 2016, we created an Instagram page for radiology education. Numerous publications in different fields have reported that Instagram "reels," introduced in 2020 as a short-form video feature, are more popular than image posts. These findings and our familiarity with Instagram prompted us to analyze our own data to better understand how image posts compared with reels when used in the context of radiology education.
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