2 citations · 3 across the 7 of their papers we have counts for
8 papers
Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
Huizi Yu, Jian Liu, Wenkong Wang +10
Patients increasingly seek medication information online, yet safety knowledge for psychiatric drugs is split between regulatory adverse-event records, which are authoritative but…
Deciphering Scientific Collaboration in Biomedical LLM Research: Dynamics, Institutional Participation, and Resource Disparities
Lingyao Li, Zhijie Duan, Xuexin Li +4
Large language models (LLMs) are increasingly transforming biomedical discovery and clinical innovation, yet their impact extends far beyond algorithmic revolution-LLMs are restruc…
Towards Trustworthy AI: Characterizing User-Reported Risks across LLMs "In the Wild"
Lingyao Li, Renkai Ma, Zhaoqian Xue +1
While Large Language Models (LLMs) are rapidly integrating into daily life, research on their risks often remains lab-based and disconnected from the problems users encounter "in t…
Crowdsourcing-Based Knowledge Graph Construction for Drug Side Effects Using Large Language Models with an Application on Semaglutide
Zhijie Duan, Kai Wei, Zhaoqian Xue +5
Social media is a rich source of real-world data that captures valuable patient experience information for pharmacovigilance. However, mining data from unstructured and noisy socia…
Patients Speak, AI Listens: LLM-based Analysis of Online Reviews Uncovers Key Drivers for Urgent Care Satisfaction
Xiaoran Xu, Zhaoqian Xue, Chi Zhang +7
Investigating the public experience of urgent care facilities is essential for promoting community healthcare development. Traditional survey methods often fall short due to limite…
Toward Equitable Access: Leveraging Crowdsourced Reviews to Investigate Public Perceptions of Health Resource Accessibility
Zhaoqian Xue, Guanhong Liu, Chong Zhang +7
Monitoring health resource disparities during public health crises is critical, yet traditional methods, like surveys, lack the requisite speed and spatial granularity. This study…