# Streamlining IRB review of AI human subjects research (AIHSR): the three-stage framework

## TL;DR

This paper introduces a three-stage framework to streamline IRB review of AI human subjects research, ensuring ethical oversight and risk management.

## Contribution

The novel Three-Stage Framework provides a risk-based model for aligning IRB review with AI development stages and potential human impact.

## Key findings

- The Three-Stage Framework aligns IRB review with AI development maturity and risk levels.
- The framework supports adaptive innovation while maintaining ethical and regulatory compliance.
- It helps IRBs manage AI risks and accelerate research without compromising human subject protection.

## Abstract

Oversight of Artificial Intelligence in Human Subjects Research (AI HSR) presents unique challenges. These challenges arise from both the non-linear and iterative nature of AI development, as well as from the way AI shifts risk from individual research subjects to larger populations affected by AI-driven decisions and data handling. Traditional Institutional Review Boards (IRBs) often struggle to keep pace with these changes, which can lead to gaps in risk assessment and delays in the review process. There is a growing need for transparent, repeatable methods to manage AI risk in healthcare. This paper introduces the Three-Stage Framework, a risk-based oversight model designed to align ethical and regulatory review with an AI project’s stage of maturity and potential human impact. By aligning the level and timing of IRB review with the types of risks present at each stage of AI system development, the framework supports appropriate regulatory pathways and documents expectations while maintaining effective protection of human subjects. Through gradual, stage-appropriate documentation, the approach supports responsible and adaptive innovation while preparing AI systems for safe and ethical use across biomedical, social, behavioral, and educational domains. This approach prepares AI systems for safe and ethical use, accelerates compliant research, and helps IRBs and institutions maintain trustworthiness while protecting human subjects.