2 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
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…