Artificial intelligence promises to accelerate drug discovery, but most AI initiatives in biopharma fail before a model is ever trained. The reason is not algorithms. It is data.
In this white paper, BullFrog AI explains why fragmented, inconsistent, and poorly contextualized biomedical data silently undermines modern analytics. Drawing from an industry webinar led by Dr. Juan Felipe Beltrán, the paper outlines the real failure modes that prevent AI from delivering trustworthy biological insight and presents a practical, defensible framework for data harmonization.
Readers will learn:
- Why raw clinical and omics data cannot reliably support AI without clinically meaningful derived features
- How category entropy and inconsistent labeling erode statistical power and distort subgroup analysis
- Why critical clinical insights remain “trapped” in unstructured documents such as PDFs and scanned trial archives
- How unconstrained AI tools can amplify error instead of insight without schema-driven guardrails
The paper introduces a three-pillar harmonization framework focused on engineered clinical features, harmonized categorical schemas, and validated structuring of unstructured clinical data. It also details a real-world case example where a 10,000-page clinical trial archive was transformed into OMOP-compatible, analysis-ready datasets, enabling downstream AI and ML for the first time.
For biopharma, biotech, CRO, and translational research teams, this white paper reframes data harmonization not as a preprocessing step, but as the foundation for reproducible, explainable, and biologically grounded AI.