Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research

Vinita Chauhan; Olivier Armant; Étienne Blanc; Imène Garali; Miroslava Čuperlović‐Culf; Y Wang

doi:10.1080/09553002.2026.2618532

February 3, 2026International Journal of Radiation Biology

Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research

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Key Points

The central aim is to establish a methodological foundation for AI-assisted adverse outcome pathways in radiation research.
Reviewing existing methodologies related to AI and AOPs
Identifying gaps in current AOP development
Proposing ways to integrate biological and experimental data using AI/ML
AI/ML can significantly speed up the development of adverse outcome pathways
Enhanced organization and prioritization of biological data is achievable
Integration of diverse experimental data can be facilitated through AI technologies

Implication

Reviews the development of AI-assisted adverse outcome pathways in radiation, suggesting future enhancements for data integration.

Abstract

While fully AI-assisted radiation AOPs remain conceptual, the review provides a methodological foundation for their future development. AI/ML offers a means to accelerate radiation AOP development, facilitating systematic organization, integration, and prioritization of biological and experimental data.

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Cite This Study

Chauhan et al. (2026) studied this question.

synapsesocial.com/papers/698584f98f7c464f230083d9 https://doi.org/10.1080/09553002.2026.2618532

View Full Paper

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Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research

Key Points

Implication

Abstract

Discussion

Cite This Study

Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research

Key Points

Implication

Abstract

Discussion

Cite This Study

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