Publications by authors named "Yuangengshuo Wang"
Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs' evolution through improved computing power and data.
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- The study reviewed the use of large language models (LLMs) in medicine, analyzing 550 studies and highlighting their impact on diagnostics, medical writing, education, and project management.
- LLMs improved tasks like drafting medical documents and enhancing communication between doctors and patients, but challenges such as limited contextual understanding and the danger of over-reliance were noted.
- Future research should address multimodal LLMs, deeper algorithm insights, and ensuring their responsible integration into healthcare practices.
Objective: This study aimed to investigate the relationship between the TyG (Triglyceride-glucose index) and the prognosis of patients with HOCM (hypertrophic obstructive cardiomyopathy) without diabetes.
Research Design And Methods: A total of 713 eligible patients with HOCM were enrolled in this study and divided into two groups based on treatment: an invasive treatment group (n = 461) and a non-invasive treatment group (n = 252). The patients in both two groups were then divided into three groups based on their TyG index levels.
Background: The remnant-like particle cholesterol (RLP-C) has been demonstrated to be associated with residual cardiovascular risk. The meta-analysis aimed to evaluate the impact of baseline RLP-C on the incidence of major cardiovascular adverse events (MACEs) in patients with coronary artery disease (CAD). Methods: A systematic literature search was performed in PubMed and Embase electronic databases from the inception of the databases through 1 October 2022.
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