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20232026
most citedA Study of Generative Large Language Model for Medical Research and Healthcare

400 citations · 513 across the 22 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

Exploring Recurrent-Depth Language Models for Multitask Clinical Text Processing

Xi Yang, Guodong Liu, Cheng Peng +7

Clinical natural language processing includes diverse tasks such as information extraction, classification, and question answering. Developing one model for all tasks could simplif…

cs.CL2026

An Agentic Generative Large Language Model for Treatment Planning of Colorectal Cancer

Mengxian Lyu, Cheng Peng, Tim Jang +18

Treatment planning in precision oncology requires synthesizing heterogeneous patient information with rapidly evolving clinical guidelines to ensure guideline-concordant care. Whil…

cs.CL2026

Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives

Ziyi Chen, Mengxian Lyu, Cheng Peng +1

Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures. Recent breakthroughs in large language…

cs.CL2026

A Parameter-Efficient Transfer Learning Approach through Multitask Prompt Distillation and Decomposition for Clinical NLP

Cheng Peng, Mengxian Lyu, Ziyi Chen +1

Existing prompt-based fine-tuning methods typically learn task-specific prompts independently, imposing significant computing and storage overhead at scale when deploying multiple…

cs.CL2026

Countering Catastrophic Forgetting of Large Language Models for Better Instruction Following via Weight-Space Model Merging

Mengxian Lyu, Cheng Peng, Ziyi Chen +3

Large language models have been adopted in the medical domain for clinical documentation to reduce clinician burden. However, studies have reported that LLMs often "forget" a signi…

cs.CL2026

MedGPT-oss: Training a General-Purpose Vision-Language Model for Biomedicine

Kai Zhang, Zhengqing Yuan, Cheng Peng +10

Biomedical multimodal assistants have the potential to unify radiology, pathology, and clinical-text reasoning, yet a critical deployment gap remains: top-performing systems are ei…