Authors
Thomas Hartung
Published in
ALTEX. Volume 43. Issue 3. Pages 397-412.
Abstract
Public health decisions are uniquely difficult, weighing population benefits against harms, equity, resource constraints, and feasibility, often under deep uncertainty. The GRADE Evidence-to- Decision (EtD) framework, rooted in evidence-based medicine, offers a transparent route from evidence to action through twelve explicit criteria, and has recently been adapted for environmental and occupational health. A Human Exposome Project would generate evidence of a volume and complexity that breaks the manual assumptions on which EtD was built. I argue that agentic artificial intelligence, autonomous agents orchestrating multi-step scientific workflows, can operationalize each EtD criterion and make exposome-scale decision-making feasible, but only if it inherits the rigor of the evidence-based disciplines it is asked to accelerate. Six families of agents (evidence extraction, risk-of-bias assessment, uncertainty quantification, causality reasoning, cost-outcome analysis, and post-deployment validation) map cleanly onto the EtD criteria. Five governance requirements (traceability, versioning, context-of-use benchmarking, honest uncertainty, and human accountability) separate an evidence engine from a confident hallucination machine. The exposome demands nothing less.
PMID:
42478148
Bibliographic data and abstract were imported from PubMed on 21 Jul 2026.
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