Clinical Trial
Clinical trials are research studies designed to evaluate the safety and efficacy of new medical interventions, aiming to accelerate the development of effective treatments. Current research emphasizes leveraging artificial intelligence, particularly large language models (LLMs) and deep learning networks, to improve various aspects of the trial process, including target selection, design optimization, patient recruitment, and outcome prediction. These AI-driven approaches offer the potential to significantly reduce costs, improve efficiency, and enhance the overall success rate of clinical trials, ultimately benefiting both researchers and patients.
Papers
Panacea: A foundation model for clinical trial search, summarization, design, and recruitment
Jiacheng Lin, Hanwen Xu, Zifeng Wang, Sheng Wang, Jimeng Sun
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design
Nafis Neehal, Bowen Wang, Shayom Debopadhaya, Soham Dan, Keerthiram Murugesan, Vibha Anand, Kristin P. Bennett
PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models
Shashi Kant Gupta, Aditya Basu, Mauro Nievas, Jerrin Thomas, Nathan Wolfrath, Adhitya Ramamurthi, Bradley Taylor, Anai N. Kothari, Regina Schwind, Therica M. Miller, Sorena Nadaf-Rahrov, Yanshan Wang, Hrituraj Singh
ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning
Ling Yue, Sixue Xing, Jintai Chen, Tianfan Fu