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
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
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials
Anna L. Trella, Kelly W. Zhang, Inbal Nahum-Shani, Vivek Shetty, Iris Yan, Finale Doshi-Velez, Susan A. Murphy
From RAGs to riches: Using large language models to write documents for clinical trials
Nigel Markey, Ilyass El-Mansouri, Gaetan Rensonnet, Casper van Langen, Christoph Meier