Objective: The use of Natural Language Processing (NLP) has attracted increased interest in healthcare with various potential applications including identification and extraction of health information, development of chatbots and virtual assistants. The aim of this comprehensive literature review was to provide an overview of NLP applications in vascular surgery, identify current limitations, and discuss future perspectives in the field.
Data Sources: The MEDLINE database was searched on April 2023.
Review Methods: The database was searched using a combination of keywords to identify studies reporting the use of NLP and chatbots in three main vascular diseases. Keywords used included Natural Language Processing, chatbot, chatGPT, aortic disease, carotid, peripheral artery disease, vascular, and vascular surgery.
Results: Given the heterogeneity of study design, techniques, and aims, a comprehensive literature review was performed to provide an overview of NLP applications in vascular surgery. By enabling identification and extraction of information on patients with vascular diseases, such technology could help to analyse data from healthcare information systems to provide feedback on current practice and help in optimising patient care. In addition, chatbots and NLP driven techniques have the potential to be used as virtual assistants for both health professionals and patients.
Conclusion: While Artificial Intelligence and NLP technology could be used to enhance care for patients with vascular diseases, many challenges remain including the need to define guidelines and clear consensus on how to evaluate and validate these innovations before their implementation into clinical practice.
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http://dx.doi.org/10.1016/j.ejvsvf.2023.09.002 | DOI Listing |
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Department of Pediatric Rheumatology, Istanbul Medeniyet University, Istanbul, Turkey.
Chronic non-bacterial osteomyelitis (CNO) is an inflammatory bone disease, usually diagnosed in childhood. It is characterized by the presence of multifocal or unifocal osteolytic lesions that can cause bone pain and soft tissue swelling. CNO is known to have soft tissue involvement.
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January 2025
Saint Michael's Hospital, University of Toronto, Toronto, Canada.
Africa is currently facing unprecedented growth in its cancer burden. Training an adequate number of skilled physicians is critical to addressing this challenge. We examine African oncology faculty's professional development (PD) activities, associated barriers, enablers, satisfaction levels, and highlight the implications for improving the quality of the oncology faculty workforce in SSA.
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November 2024
Department of Computer Science, Hunan University, Changsha 410008, China.
Recently, the impressive performance of large language models (LLMs) on a wide range of tasks has attracted an increasing number of attempts to apply LLMs in drug discovery. However, molecule optimization, a critical task in the drug discovery pipeline, is currently an area that has seen little involvement from LLMs. Most of existing approaches focus solely on capturing the underlying patterns in chemical structures provided by the data, without taking advantage of expert feedback.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Washington University School of Medicine, St. Louis, MO, USA.
Background: Alzheimer disease (AD) involves neurodegenerative disorders with progressive cognitive decline. Atypical presentations like Posterior Cortical Atrophy (PCA) and Logopenic Variant Primary Progressive Aphasia (lvPPA) exhibit distinct clinical profiles. PCA affects the posterior parietal and occipital lobes, causing visuospatial deficits, while lvPPA manifests as language impairment in the temporoparietal region.
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December 2024
Miin Wu School of Computing, National Cheng Kung University, Tainan, Taiwan.
Background: Continuous speech analysis is considered as an efficient and convenient approach for early detection of Alzheimer's Disease (AD). However, the traditional approach generally requires human transcribers to transcribe audio data accurately. This study applied automatic speech recognition (ASR) in conjunction with natural language processing (NLP) techniques to automatically extract linguistic features from Chinese speech data.
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