Chat-IRB? How application-specific language models can enhance research ethics review.

Area of Science:

• Bioethics and Research Oversight
• Artificial Intelligence in Healthcare
• Human Subjects Research

Background:

• Institutional Review Boards (IRBs) are vital for ethical human subjects research.
• Current IRB processes face significant challenges including inconsistency, delays, and inefficiencies.
• Existing review mechanisms struggle to keep pace with the complexities of modern research.

Purpose of the Study:

• To propose the development and implementation of application-specific large language models (LLMs) for IRB review.
• To enhance the efficiency, consistency, and quality of ethical review processes.
• To explore AI-driven solutions for improving research oversight while maintaining human judgment.

Main Methods:

• Fine-tuning LLMs on IRB-specific literature and institutional datasets.
• Equipping LLMs with retrieval capabilities for up-to-date, context-relevant information.
• Outlining potential applications: pre-review screening, preliminary analysis, consistency checking, and decision support.

Main Results:

• LLMs can potentially streamline IRB workflows by automating preliminary tasks.
• AI can aid in identifying inconsistencies and ensuring adherence to ethical guidelines.
• The proposed system aims to support, not replace, human oversight in critical decision-making.

Conclusions:

• IRB-specific LLMs offer a promising approach to enhance research ethics oversight.
• Addressing concerns regarding accuracy, context sensitivity, and transparency is crucial for successful implementation.
• Pilot studies are recommended to evaluate the feasibility and impact of LLM integration in IRB processes.