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http://dx.doi.org/10.1038/s41597-025-05786-z | DOI Listing |
Acta Neurochir (Wien)
September 2025
Department of Neurosurgery, Istinye University, Istanbul, Turkey.
Background: Recent studies suggest that large language models (LLMs) such as ChatGPT are useful tools for medical students or residents when preparing for examinations. These studies, especially those conducted with multiple-choice questions, emphasize that the level of knowledge and response consistency of the LLMs are generally acceptable; however, further optimization is needed in areas such as case discussion, interpretation, and language proficiency. Therefore, this study aimed to evaluate the performance of six distinct LLMs for Turkish and English neurosurgery multiple-choice questions and assess their accuracy and consistency in a specialized medical context.
View Article and Find Full Text PDFJMIR Med Inform
August 2025
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan, 81 3-3815-5411.
Background: Recent advances in large language models have highlighted the need for high-quality multilingual medical datasets. Although Japan is a global leader in computed tomography (CT) scanner deployment and use, the absence of large-scale Japanese radiology datasets has hindered the development of specialized language models for medical imaging analysis. Despite the emergence of multilingual models and language-specific adaptations, the development of Japanese-specific medical language models has been constrained by a lack of comprehensive datasets, particularly in radiology.
View Article and Find Full Text PDFBehav Res Methods
August 2025
Department of Special Education and Counselling, The Education University of Hong Kong, Hong Kong, China.
Can J Exp Psychol
September 2025
Department of Psychology, University of Waterloo.
We compared the effectiveness of encoding techniques in learning a character-based language. During an encoding phase, participants naive to Korean were shown 40 English-Korean word pairs in Experiment 1 (80 in Experiment 2) and asked to either repeat aloud (produce) the Korean pronunciation or copy the image of the Korean character. Recognition was later assessed in two ways: On an auditory test, participants selected the correct pairing from two audio clips of Korean words, and on a visual test, they selected the correct pairing from among two visually presented Korean characters.
View Article and Find Full Text PDFSci Data
August 2025
Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, 430079, China.