BullFrog AI Releases White Paper on Data Harmonization in Life Sciences

BullFrog AI Holdings publishes a white paper titled, "Data Harmonization: The Hidden Prerequisite for Reliable AI/ML in Life Sciences," with a discussion on how BullFrog AI's bfPREP immediately recognizes reliable patterns to transform raw biomedical data into harmonized, clinically contextualized formats, providing reliable insights and trustworthy analytical assets to assist in drug development. "The rush to apply AI in biopharma drug development has resulted in many AI initiatives that fail, not due to the algorithm, but due to the resulting analysis that reflect data processing idiosyncrasies rather than biology," said CEO Vin Singh. "The white paper discusses how BullFrog makes messy biomedical data usable, with our experienced data team quick to recognize the typical state of the underlying data, which is often fragmented across sources and trapped in formats that resist automated processing. Our proprietary bfPREP addresses all this by harmonizing and standardizing raw data into clean, analysis-ready datasets so that teams can comfortably trust their inputs." The white paper outlines where modern AI pipelines break in life sciences and presents a practical harmonization framework built on three pillars: engineering clinically meaningful derived features, producing reliable categorical variables and harmonized schemas, and transforming unstructured clinical documents into analysis-ready tables.