Hybrid Intelligence in Decision-Making for Human Health and the Environment

The growing complexity of challenges affecting human health and the environment requires new approaches that combine human expertise with advanced computational intelligence. Hybrid intelligence, integrating human judgment with artificial intelligence (AI), offers a powerful framework for supporting evidence-based decision-making in areas ranging from chemical safety and environmental protection to personalized health.

Recent advances in machine learning, chemoinformatics, and predictive modeling have enabled the development of computational systems capable of analyzing large and heterogeneous datasets, identifying hidden patterns, and generating reliable predictions for complex biological and environmental processes. These technologies support the assessment of chemical hazards, the prediction of toxicological effects, the design of safer chemicals and materials, and the reduction of animal testing through alternative predictive methodologies.

The integration of predictive models, AI, and digital twin technologies is transforming scientific and regulatory decision-making. By combining computational predictions with human expertise, these approaches enable more transparent, efficient, and informed decisions under conditions of uncertainty.

Hybrid intelligence represents a new paradigm in which human expertise and artificial intelligence complement each other, enabling more responsible, sustainable, and evidence-based decisions for the protection of human health and the environment.