Institutional review boards (IRBs) play a crucial role in ensuring the ethical conduct of human subjects research, but face challenges including inconsistency, delays, and inefficiencies. We propose the development and implementation of application-specific large language models (LLMs) to facilitate IRB review processes. These IRB-specific LLMs would be fine-tuned on IRB-specific literature and institutional datasets, and equipped with retrieval capabilities to access up-to-date, context-relevant information. We outline potential applications, including pre-review screening, preliminary analysis, consistency checking, and decision support. While addressing concerns about accuracy, context sensitivity, and human oversight, we acknowledge remaining challenges such as over-reliance on AI and the need for transparency. By enhancing the efficiency and quality of ethical review while maintaining human judgment in critical decisions, IRB-specific LLMs offer a promising tool to improve research oversight. We call for pilot studies to evaluate the feasibility and impact of this approach.
@article{arxiv.2501.10741,
title = {Development of Application-Specific Large Language Models to Facilitate Research Ethics Review},
author = {Sebastian Porsdam Mann and Joel Seah Jiehao and Stephen R. Latham and Julian Savulescu and Mateo Aboy and Brian D. Earp},
journal= {arXiv preprint arXiv:2501.10741},
year = {2025}
}