Ethical oversight of AI-driven paediatric trials: a proactive, risk-sensitive interim review model

Abstract

Background: Artificial intelligence (AI)-driven paediatric trials pose novel challenges for institutional review boards (IRBs), as traditional annual continuing review frameworks are often inadequate for evolving algorithmic and data-related risks. International and national regulations provide only limited guidance on how to design proactive, risk-sensitive interim oversight mechanisms for such research. Objective: To develop and illustrate a risk-sensitive interim review model that strengthens participant protection and procedural fairness in AI-enabled paediatric research. Methods: = 100, aged 3-7 years) is presented as a worked example rather than empirical data collection. Results: The model comprises five interlocking components: (1) scheduled, risk-based interim reviews and audits; (2) structured deviation-triggered response procedures; (3) mechanisms for re-consent and ongoing communication; (4) continuous ethics and protocol training; and (5) transparent, auditable documentation and IRB-investigator communication. Application of the proposed model to the Taiwanese worked example illustrates how a structured, risk-sensitive interim review process can support the identification of informed-consent and eligibility-screening deviations, facilitate targeted corrective training, and promote routine documentation monitoring. Conclusions: A proactive, risk-sensitive interim review model can support IRBs in shifting from reactive annual oversight to continuous, adaptive governance aligned with AI-specific risk profiles. The model offers a transferable, principle-based template for strengthening ethical oversight of AI-driven pediatric trials across diverse regulatory and cultural settings.

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