Pharmaceutical companies cannot leverage AI on real-world clinical data because inconsistent medical terminology breaks AI reasoning

Pharma organizations have invested heavily in AI models and accumulated massive real-world datasets, but cannot extract actionable insights because clinical data uses inconsistent, ambiguous terminology that AI systems cannot reliably interpret. This semantic layer problem prevents drug developers, clinical researchers, and data scientists from using AI to accelerate drug discovery, safety monitoring, and patient outcomes analysis—forcing them to rely on manual data curation and slower traditional analysis methods.