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ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.
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http://dx.doi.org/10.1186/s40634-023-00700-1 | DOI Listing |
Turk Kardiyol Dern Ars
September 2025
Department of Cardiology, Muğla Sıtkı Koçman University, School of Medicine, Muğla, Türkiye.
Objective: Management of aortic stenosis (AS) requires integrating complex clinical, imaging, and risk stratification data. Large language models (LLMs) such as ChatGPT and Gemini AI have shown promise in healthcare, but their performance in valvular heart disease, particularly AS, has not been thoroughly assessed. This study systematically compared ChatGPT and Gemini AI in addressing guideline-based and clinical scenario questions related to AS.
View Article and Find Full Text PDFFront Sociol
August 2025
Laboratory of Anthropology of Contemporary Worlds (LAMC), Faculty of Philosophy and Social Sciences, Institute of Sociology, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Contemporary debates about artificial intelligence (AI) still treat automation as a straightforward substitution of human labor by machines. Drawing on Goffman's dramaturgical sociology, this paper reframes AI in the workplace as rather than automation. We argue that the central-but routinely overlooked-terrain of struggle is symbolic-interactional: workers continuously stage, conceal, and re-negotiate what counts as "real" work and professional competence.
View Article and Find Full Text PDFJAMIA Open
October 2025
Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, United States.
Objectives: Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations.
Materials And Methods: A total of 220 RHC notes were randomly selected for pipeline development and 200 for validation from the Pulmonary Vascular Disease Registry.
Clin Ophthalmol
August 2025
University of Virginia School of Medicine, Charlottesville, VA, USA.
Purpose: Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. Despite the importance of early DR detection, only 60% of patients with diabetes receive recommended annual screenings due to limited eye care provider capacity. FDA-approved AI systems were developed to meet the growing demand for DR screening; however, high costs and specialized equipment limit accessibility.
View Article and Find Full Text PDFQual Health Res
September 2025
Department of Nursing and Health Promotion, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
The launch of ChatGPT in November 2022 accelerated discussions and research into whether base large language models (LLMs) could increase the efficiency of qualitative analysis phases or even replace qualitative researchers. Reflexive thematic analysis (RTA) is a commonly used method for qualitative text analysis that emphasizes the researcher's subjectivity and reflexivity to enable a situated, in-depth understanding of knowledge generation. Researchers appear optimistic about the potential of LLMs in qualitative research; however, questions remain about whether base models can meaningfully contribute to the interpretation and abstraction of a dataset.
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