6 papers
The Consistency Illusion: How Multi-Agent Debate Hides Reasoning Misalignment
Xiaoyang Wang, Christopher C. Yang
Multi-agent LLM systems for medical question answering often treat consensus as a reliability signal: if multiple agents agree on an answer, it is presumed trustworthy. However, an…
MediHive: A Decentralized Agent Collective for Medical Reasoning
Xiaoyang Wang, Christopher C. Yang
Large language models (LLMs) have revolutionized medical reasoning tasks, yet single-agent systems often falter on complex, interdisciplinary problems requiring robust handling of…
Automated Clinical Problem Detection from SOAP Notes using a Collaborative Multi-Agent LLM Architecture
Yeawon Lee, Xiaoyang Wang, Christopher C. Yang
Accurate interpretation of clinical narratives is critical for patient care, but the complexity of these notes makes automation challenging. While Large Language Models (LLMs) show…
MoE-Health: A Mixture of Experts Framework for Robust Multimodal Healthcare Prediction
Xiaoyang Wang, Christopher C. Yang
Healthcare systems generate diverse multimodal data, including Electronic Health Records (EHR), clinical notes, and medical images. Effectively leveraging this data for clinical pr…
Balancing Fairness and Performance in Healthcare AI: A Gradient Reconciliation Approach
Xiaoyang Wang, Christopher C. Yang
The rapid growth of healthcare data and advances in computational power have accelerated the adoption of artificial intelligence (AI) in medicine. However, AI systems deployed with…
Enhancing Multi-Attribute Fairness in Healthcare Predictive Modeling
Xiaoyang Wang, Christopher C. Yang
Artificial intelligence (AI) systems in healthcare have demonstrated remarkable potential to improve patient outcomes. However, if not designed with fairness in mind, they also car…