244 citations · 508 across the 40 of their papers we have counts for
9 papers · 1 filter
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges
Zifeng Wang, Hanyin Wang, Benjamin Danek +6
The integration of Large Language Models (LLMs) into medical applications has sparked widespread interest across the healthcare industry, from drug discovery and development to cli…
SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning
Trisha Das, Zifeng Wang, Afrah Shafquat +4
Each year, hundreds of clinical trials are conducted to evaluate new medical interventions, but sharing patient records from these trials with other institutions can be challenging…
Can Large Language Models Replace Data Scientists in Biomedical Research?
Zifeng Wang, Benjamin Danek, Ziwei Yang +2
Data science plays a critical role in biomedical research, but it requires professionals with expertise in coding and medical data analysis. Large language models (LLMs) have shown…
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…
Panacea: A foundation model for clinical trial search, summarization, design, and recruitment
Jiacheng Lin, Hanwen Xu, Zifeng Wang +2
Clinical trials are fundamental in developing new drugs, medical devices, and treatments. However, they are often time-consuming and have low success rates. Although there have bee…
Accelerating Clinical Evidence Synthesis with Large Language Models
Zifeng Wang, Lang Cao, Benjamin Danek +3
Synthesizing clinical evidence largely relies on systematic reviews of clinical trials and retrospective analyses from medical literature. However, the rapid expansion of publicati…
TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
Pengcheng Jiang, Cao Xiao, Zifeng Wang +3
The advent of large language models (LLMs) has significantly advanced natural language processing tasks like text summarization. However, their large size and computational demands…
GenRES: Rethinking Evaluation for Generative Relation Extraction in the Era of Large Language Models
Pengcheng Jiang, Jiacheng Lin, Zifeng Wang +2
The field of relation extraction (RE) is experiencing a notable shift towards generative relation extraction (GRE), leveraging the capabilities of large language models (LLMs). How…
PILOT: Legal Case Outcome Prediction with Case Law
Lang Cao, Zifeng Wang, Cao Xiao +1
Machine learning shows promise in predicting the outcome of legal cases, but most research has concentrated on civil law cases rather than case law systems. We identified two uniqu…