Development of Application-Specific Large Language Models to Facilitate Research Ethics Review
This addresses delays and variability in research ethics review for institutions and researchers, but it is incremental as it builds on existing LLM technology for a specific domain.
The paper tackles inefficiencies and inconsistencies in institutional review board (IRB) processes by proposing application-specific large language models (LLMs) fine-tuned on IRB data to assist with tasks like pre-review screening and decision support, aiming to enhance efficiency and quality while maintaining human oversight.
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.