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Towards a Planetary Health Impact Assessment Framework: Exploring Expert Knowledge and Artificial Intelligence for a
Magdalini Stefanopoulou1, Tabea S Sonnenschein1,2,3, Florence Poulletier de Gannes4
1Institute of Risk Assessment Sciences, Utrecht University, Utrecht, the Netherlands.
Bioelectromagnetics
|December 19, 2025
Summary
This study introduces a Planetary Health Impact Assessment (PHIA) framework using knowledge graphs to explore indirect health effects of radiofrequency electromagnetic field (RF-EMF) exposure via ecosystem disruption. Expert-validated knowledge graphs offer a novel approach to understanding complex environmental health interactions.
Area of Science:
- Environmental Health Science
- Computational Biology
- Ecotoxicology
Background:
- Existing research on radiofrequency electromagnetic field (RF-EMF) exposure primarily focuses on direct health effects, neglecting indirect impacts mediated by ecosystem disruption.
- A comprehensive understanding of RF-EMF's full health implications necessitates an approach that considers ecologically mediated pathways.
Purpose of the Study:
- To propose and develop a Planetary Health Impact Assessment (PHIA) framework for evaluating both direct and indirect health effects of RF-EMF exposure.
- To explore the utility of knowledge graphs (KGs) for organizing and visualizing the complex, interdisciplinary knowledge required for PHIA.
- To assess the potential of artificial intelligence (AI) tools in generating and enhancing these KGs.
Main Methods:
- An expert-based knowledge graph (KG) was constructed collaboratively with 12 specialists, using RF-EMF from mobile telecommunications as a case study.
- An AI tool incorporating Natural Language Processing (NLP) and Deep Learning was evaluated for its ability to extract information and generate KGs from scientific literature.
- Hypothesized pathways linking RF-EMF exposure to direct and indirect health effects via ecological consequences were developed and visualized.
Main Results:
- The expert-based KG effectively organized knowledge and served as a foundational step for PHIA development.
- AI tools demonstrated rapid processing of literature and KG generation but required significant expert validation due to precision and context sensitivity limitations.
- The developed KGs identified potential gaps in current scientific literature regarding RF-EMF's ecological impacts.
Conclusions:
- Knowledge graphs provide a valuable framework for organizing complex scientific information and initiating Planetary Health Impact Assessments.
- While AI tools show promise for literature analysis, expert judgment remains crucial for validating findings and ensuring accuracy in PHIA.
- The study highlights the need for further research into the indirect health effects of RF-EMF exposure mediated through ecosystem disruption.
