9 papers
Entry-level guide to the use of large language models for medical research
Qiao Jin, Nicholas Wan, Robert Leaman +20
Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools capable of revolutionizing variou…
Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization
Gongbo Zhang, Yifan Peng, Chunhua Weng
Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge. Recent agentic RAG systems e…
Autonomous Agent-Orchestrated Digital Twins (AADT): Leveraging the OpenClaw Framework for State Synchronization in Rare Genetic Disorders
Hongzhuo Chen, Zhanliang Wang, Quan M. Nguyen +3
Background: Medical Digital Twins (MDTs) are computational representations of individual patients that integrate clinical, genomic, and physiological data to support diagnosis, tre…
CPGPrompt: Translating Clinical Guidelines into LLM-Executable Decision Support
Ruiqi Deng, Geoffrey Martin, Tony Wang +6
Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into Artificial Intelligence (AI) remains challenging. Previo…
Scalable Scientific Interest Profiling Using Large Language Models
Yilun Liang, Gongbo Zhang, Edward Sun +6
Research profiles highlight scientists' research focus, enabling talent discovery and collaborations, but are often outdated. Automated, scalable methods are urgently needed to kee…
EvidenceOutcomes: a Dataset of Clinical Trial Publications with Clinically Meaningful Outcomes
Yiliang Zhou, Abigail M. Newbury, Gongbo Zhang +4
The fundamental process of evidence extraction and synthesis in evidence-based medicine involves extracting PICO (Population, Intervention, Comparison, and Outcome) elements from b…