Prize Contributions List

Individual hospitals and clinicians generate a continuous stream of disease, diagnosis and treatment data, but no single institution can turn it into a systematic, reusable body of knowledge. This proposal outlines an AI-assisted pipeline that converts distributed, heterogeneous clinical records into a unified, freely accessible archive, which can serve both clinical diagnosis and biomedical research, and can act as empirical grounding for broader collective efforts to build comprehensive models of the human body.

Experiments often generate far more raw information than is used in the final analysis or publication. Data are usually selected for the original scientific question, while unused measurements, acquisition metadata, intermediate outputs and experimental context may remain local, poorly documented, or never become searchable. This limits reproducibility and wastes potentially valuable data precisely when biology increasingly needs large, heterogeneous datasets for AI and virtual-cell models.

Artificial intelligence (AI) is already changing radically the way scientific research is carried out. Programming, numerical calculations, literature searches, data analysis, or the exploration of alternative hypotheses are increasingly being delegated to AI systems. This allows scientist to focus on more higher level and creative-based tasks.