2 citations · 3 across the 7 of their papers we have counts for
13 papers
Large language models show systematic failures in incorporating evolving medical knowledge
Min Zeng, Zaifu Zhan, Kai Yu +1
Medical knowledge evolves as clinical guidelines revise recommendations, yet whether large language models (LLMs) faithfully incorporate such changes remains unclear. We introduce…
Large language models exhibit unreliable updating of clinical judgment as patient evidence evolves
Min Zeng, Rui Zhang
Large language models (LLMs) are increasingly explored for clinical reasoning, but whether they appropriately revise judgments as patient evidence evolves remains unclear. We evalu…
ChestPheNoT: Deployable, Auditable Label-Status-Evidence Extraction from Radiology Reports
Kai Yu, Chenyu Zhu, Zaifu Zhan +5
Structured phenotype extraction from radiology reports supports cohort construction, quality auditing, and clinical analytics, but practical deployment requires local inference and…
Can Large Language Models Self-Correct in Medical Question Answering? An Exploratory Study
Zaifu Zhan, Mengyuan Cui, Rui Zhang
Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by elici…
An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review
Zaifu Zhan, Yu Hou, Kai Yu +4
Rare diseases affect over 300 million people worldwide and are characterized by complex care pathways, limited clinical expertise, and substantial unmet communication needs through…
MedCL-Bench: Benchmarking stability-efficiency trade-offs and scaling in biomedical continual learning
Min Zeng, Shuang Zhou, Zaifu Zhan +1
Medical language models must be updated as evidence and terminology evolve, yet sequential updating can trigger catastrophic forgetting. Although biomedical NLP has many static ben…
Benchmarking GPT-5 for biomedical natural language processing
Yu Hou, Zaifu Zhan, Min Zeng +3
Biomedical literature and clinical narratives pose multifaceted challenges for natural language understanding, from precise entity extraction and document synthesis to multi-step d…
Quantized Large Language Models in Biomedical Natural Language Processing: Evaluation and Recommendation
Zaifu Zhan, Shuang Zhou, Min Zeng +6
Large language models have demonstrated remarkable capabilities in biomedical natural language processing, yet their rapid growth in size and computational requirements present a m…
Data-Efficient Biomedical In-Context Learning: A Diversity-Enhanced Submodular Perspective
Jun Wang, Zaifu Zhan, Qixin Zhang +3
Recent progress in large language models (LLMs) has leveraged their in-context learning (ICL) abilities to enable quick adaptation to unseen biomedical NLP tasks. By incorporating…
Uncertainty-Aware Large Language Models for Explainable Disease Diagnosis
Shuang Zhou, Jiashuo Wang, Zidu Xu +11
Explainable disease diagnosis, which leverages patient information (e.g., signs and symptoms) and computational models to generate probable diagnoses and reasonings, offers clear c…
Retrieval-augmented in-context learning for multimodal large language models in disease classification
Zaifu Zhan, Shuang Zhou, Xiaoshan Zhou +6
Objectives: We aim to dynamically retrieve informative demonstrations, enhancing in-context learning in multimodal large language models (MLLMs) for disease classification. Methods…
MMRAG: Multi-Mode Retrieval-Augmented Generation with Large Language Models for Biomedical In-Context Learning
Zaifu Zhan, Jun Wang, Shuang Zhou +2
Objective: To optimize in-context learning in biomedical natural language processing by improving example selection. Methods: We introduce a novel multi-mode retrieval-augmented ge…
Towards Better Multi-task Learning: A Framework for Optimizing Dataset Combinations in Large Language Models
Zaifu Zhan, Rui Zhang
To efficiently select optimal dataset combinations for enhancing multi-task learning (MTL) performance in large language models, we proposed a novel framework that leverages a neur…