2 citations · 7 across the 11 of their papers we have counts for
12 papers
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Research profiles highlight scientists' research focus, enabling talent discovery and collaborations, but are often outdated.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…
Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review
Zihan Xu, Haotian Ma, Gongbo Zhang +3
Evidence-based medicine (EBM) is at the forefront of modern healthcare, emphasizing the use of the best available scientific evidence to guide clinical decisions. Due to the sheer…
Semi-Supervised Learning from Small Annotated Data and Large Unlabeled Data for Fine-grained PICO Entity Recognition
Fangyi Chen, Gongbo Zhang, Yilu Fang +2
Objective: Extracting PICO elements -- Participants, Intervention, Comparison, and Outcomes -- from clinical trial literature is essential for clinical evidence retrieval, appraisa…
A MapReduce Approach to Effectively Utilize Long Context Information in Retrieval Augmented Language Models
Gongbo Zhang, Zihan Xu, Qiao Jin +8
While holding great promise for improving and facilitating healthcare, large language models (LLMs) struggle to produce up-to-date responses on evolving topics due to outdated know…
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…
Closing the gap between open-source and commercial large language models for medical evidence summarization
Gongbo Zhang, Qiao Jin, Yiliang Zhou +11
Large language models (LLMs) hold great promise in summarizing medical evidence. Most recent studies focus on the application of proprietary LLMs. Using proprietary LLMs introduces…
A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications
Gongbo Zhang, Yiliang Zhou, Yan Hu +3
Objectives Extraction of PICO (Populations, Interventions, Comparison, and Outcomes) entities is fundamental to evidence retrieval. We present a novel method PICOX to extract overl…
Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness
Gongbo Zhang, Qiao Jin, Denis Jered McInerney +11
Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical ev…