Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research
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.
Authors
- Miroslava Čuperlović‐Culf (ORCID: https://orcid.org/0000-0002-9483-8159)
- Olivier Armant (ORCID: https://orcid.org/0000-0001-7101-9209)
- Y Wang
- Vinita Chauhan
- Imène Garali
Institutions
- Health Canada (CA)
- Institut de Radioprotection et de Sûreté Nucléaire (FR)
- University of Ottawa (CA)
- Inserm (FR)
- Université Paris Cité (FR)
- Sorbonne Paris Cité (FR)
- Autorité de Sûreté Nucléaire (FR)
- National Research Council Canada (CA)
- Autorité de sûreté nucléaire et de radioprotection (FR)
- Canadian Nuclear Laboratories (CA)
Publication Details
- Journal
- International Journal of Radiation Biology
- Published
- 2026-02-03
- DOI
- https://doi.org/10.1080/09553002.2026.2618532
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00