Liying Huang
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A knowledge-enhanced domain-aware large language model agent for atrial fibrillation management
Abstract Large language models (LLMs) show considerable potential for atrial fibrillation (AF) management, yet current clinical applications frequently remain suboptimal due to accuracy limitations. To address these limitations, this study developed PULSE (Potentiated User-friendly LLM-driven Search Engine), a novel knowledge-enhanced, domain-aware LLM agent specifically designed to improve AF patient self-management across the entire care continuum.
Combined Bulk-Interfacial Engineering of Ultrahigh-Nickel LiNi 0.9 Co 0.05 Mn 0.05 O 2 to Regulate Electrochemical Kinetics and Phase Reversibility under Harsh Conditions Click to copy article link Article link copied!
Cite Jump to ExpandCollapse ArticleJuly 3, 2026 Combined Bulk-Interfacial Engineering of Ultrahigh-Nickel LiNi0.9Co0.05Mn0.05O2 to Regulate Electrochemical Kinetics and Phase Reversibility under Harsh Conditions Click to copy article linkArticle link copied! Liying Huang Liying Huang Guangxi Key Laboratory of Electrochemical Energy Materials, School of Chemistry and Chemical Engineering, Guangxi University, Nanning 530004, China Qianyu Zeng Qianyu Zeng Guangxi Key Laboratory of...
The effects of multitype prompt engineering for large language models in hypertension treatment decisions
Abstract The effects of various prompt engineering on Large Language Models (LLMs) performance in hypertension decision-making are not yet fully understood. We evaluate the impact of different prompt engineering on LLM performance in hypertension treatment decision-making. We conducted a two-stage validation study using 300 de-identified simulated hypertension cases based on real-world clinical scenarios.
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