Two recurrent concerns in discussions on an embodied view of cognition are the "necessity question" (i.e., is activation in modality-specific brain areas necessary for language comprehension?) and the "simulation constraint" (i.e., how do we understand language for which we lack the relevant experiences?). In the present paper we argue that the criticisms encountered by the embodied approach hinge on a cognitivist interpretation of embodiment. We argue that the data relating sensorimotor activation to language comprehension can best be interpreted as supporting a non-representationalist, enactivist model of language comprehension, according to which language comprehension can be described as procedural knowledge - knowledge how, not knowledge that - that enables us to interact with others in a shared physical world. The enactivist view implies that the activation of modality-specific brain areas during language processing reflects the employment of sensorimotor skills and that language comprehension is a context-bound phenomenon. Importantly, an enactivist view provides an embodied approach of language, while avoiding the problems encountered by a cognitivist interpretation of embodiment.
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http://dx.doi.org/10.3389/fpsyg.2010.00234 | DOI Listing |
Medicine (Baltimore)
January 2025
School of Medicine, University of California, Irvine, Irvine, CA.
This study evaluates the efficacy of GPT-4, a Large Language Model, in simplifying medical literature for enhancing patient comprehension in glaucoma care. GPT-4 was used to transform published abstracts from 3 glaucoma journals (n = 62) and patient education materials (Patient Educational Model [PEMs], n = 9) to a 5th-grade reading level. GPT-4 was also prompted to generate de novo educational outputs at 6 different education levels (5th Grade, 8th Grade, High School, Associate's, Bachelor's and Doctorate).
View Article and Find Full Text PDFAdv Skin Wound Care
January 2025
At the Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, United States, Adrian Chen, BS, Aleksandra Qilleri, BS, and Timothy Foster, BS, are Medical Students. Amit S. Rao, MD, is Project Manager, Department of Surgery, Wound Care Division, Northwell Wound Healing Center and Hyperbarics, Northwell Health, Hempstead. Sandeep Gopalakrishnan, PhD, MAPWCA, is Associate Professor and Director, Wound Healing and Tissue Repair Analytics Laboratory, School of Nursing, College of Health Professions, University of Wisconsin-Milwaukee. Jeffrey Niezgoda, MD, MAPWCA, is Founder and President Emeritus, AZH Wound Care and Hyperbaric Oxygen Therapy Center, Milwaukee, and President and Chief Medical Officer, WebCME, Greendale, Wisconsin. Alisha Oropallo, MD, is Professor of Surgery, Donald and Barbara Zucker School of Medicine and The Feinstein Institutes for Medical Research, Manhasset New York; Director, Comprehensive Wound Healing Center, Northwell Health; and Program Director, Wound and Burn Fellowship program, Northwell Health.
Generative artificial intelligence (AI) models are a new technological development with vast research use cases among medical subspecialties. These powerful large language models offer a wide range of possibilities in wound care, from personalized patient support to optimized treatment plans and improved scientific writing. They can also assist in efficiently navigating the literature and selecting and summarizing articles, enabling researchers to focus on impactful studies relevant to wound care management and enhancing response quality through prompt-learning iterations.
View Article and Find Full Text PDFStrahlenther Onkol
January 2025
Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
Background: This study aims to evaluate the capabilities and limitations of large language models (LLMs) for providing patient education for men undergoing radiotherapy for localized prostate cancer, incorporating assessments from both clinicians and patients.
Methods: Six questions about definitive radiotherapy for prostate cancer were designed based on common patient inquiries. These questions were presented to different LLMs [ChatGPT‑4, ChatGPT-4o (both OpenAI Inc.
Eur Arch Otorhinolaryngol
January 2025
Department of Otorhinolaryngology, University Medical Center of the Johannes Gutenberg-University Mainz, Langenbeckstraße 1, 55131, Mainz, Germany.
Introduction: Tumor boards are a cornerstone of modern cancer treatment. Given their advanced capabilities, the role of Large Language Models (LLMs) in generating tumor board decisions for otorhinolaryngology (ORL) head and neck surgery is gaining increasing attention. However, concerns over data protection and the use of confidential patient information in web-based LLMs have restricted their widespread adoption and hindered the exploration of their full potential.
View Article and Find Full Text PDFHealthcare (Basel)
December 2024
Department of Speech Therapy, School of Health Sciences, University of Ioannina, 455 00 Ioannina, Greece.
This study presents a comprehensive investigation into the correlation between Rare Diseases and Syndromes (RDS) and the dysphagic disorders manifested during childhood and adulthood in affected patients. Dysphagia is characterized by difficulty or an inability to swallow food of any consistency, as well as saliva or medications, from the oral cavity to the stomach. RDS often present with complex and heterogeneous clinical manifestations, making it challenging to develop standardized diagnostic and therapeutic approaches.
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