ArticleFrontiers in digital health2026
Ethical oversight of AI-driven paediatric trials: a proactive, risk-sensitive interim review model.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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2 authors.
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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: A conceptual normative analysis was conducted, integrating four ethical principles-protection, proportionality, respect for autonomy and assent, and procedural justice-with international guidelines [International Conference on Harmonisation-Good Clinical Practice ICH-GCP, Council for International Organizations of Medical Sciences (CIOMS), and the Declaration of Helsinki] and Taiwanese regulations. From this synthesis, a five-component proactive interim review model was developed. To illustrate the model's practical application and feasibility, a Taiwanese IRB-mandated interim review of an AI-assisted pediatric speech-therapy trial ( 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.