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Generative artificial intelligence (AI), particularly large language models (LLMs), has emerged as a transformative technology across all medical specialties, including musculoskeletal (MSK) oncology. These models, such as ChatGPT and others, can process natural language, synthesize vast amounts of information, and generate contextually relevant outputs that resemble human communication. In orthopedic oncology, LLMs show promise in facilitating literature reviews, enhancing patient education, and supporting clinical decision-making by analyzing multidimensional data while providing improved logic-based reasoning. Additionally, they can assist in radiological and pathological workflows by interpreting imaging reports and drafting diagnostic summaries, thereby increasing efficiency and accuracy. In the near future, they are expected to aid in real-time patient follow-up and counseling, information transfer, efficient diagnostics, and even continuous surgical education and assistance. Despite their potential, challenges such as the risk of inaccuracies and biases, as well as the necessity for continuous supervision, warrant a cautious and responsible integration into clinical practice. This narrative review examines the current applications of LLMs in MSK oncology, their limitations, and their future potential in shaping precision medicine and equitable healthcare delivery.
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http://dx.doi.org/10.1016/j.jcot.2025.103161 | DOI Listing |
J Neurophysiol
September 2025
School of Psychological and Cognitive Sciences, Peking University, Beijing, China.
Limiting cognitive resources negatively impacts motor learning, but its cognitive mechanism is still unclear. Previous studies failed to differentiate its effect on explicit (or cognitive) and implicit (or procedural) aspects of motor learning. Here, we designed a dual-task paradigm requiring participants to simultaneously perform a visual working memory task and a visuomotor rotation adaptation task to investigate how cognitive load differentially impacted explicit and implicit motor learning.
View Article and Find Full Text PDFPLoS One
September 2025
School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America.
Background: Financial hardship (including financial stress, financial strain, asset depletion, and financial toxicity) is a highly relevant construct among the 6.9 million people living with Alzheimer's disease and related dementias (ADRD) in the United States and their family networks. This scoping review will identify existing measures and approaches for capturing financial strain among these families.
View Article and Find Full Text PDFJ Anim Sci
September 2025
Department of Animal Science, South Dakota State University, Brookings, SD 57007, USA.
Flaxseed oil contains elevated levels of omega-3 fatty acids (n-3 FA), which have been shown to impact reproductive performance. This study aimed to determine the effects of a flaxseed oil-based supplement on reproductive parameters, feeding behavior, and lipid profile in beef heifers. Sixty Angus and Simmental × Angus heifers (14 months old ± 2 months), blocked by full body weight (BW; 396.
View Article and Find Full Text PDFActa 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 PDFRadiology
September 2025
Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115.
Despite the rapid growth of Food and Drug Administration-cleared artificial intelligence (AI)- and machine learning-enabled medical devices for use in radiology, current tools remain limited in scope, often focusing on narrow tasks and lacking the ability to comprehensively assist radiologists. These narrow AI solutions face limitations in financial sustainability, operational efficiency, and clinical utility, hindering widespread adoption and constraining their long-term value in radiology practice. Recent advances in generative and multimodal AI have expanded the scope of image interpretation, prompting discussions on the development of generalist medical AI.
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