In this file photo taken on January 27, 2021, Palestinian doctors and technicians work at the IVF laboratory at the Razan Center fertility clinic in Nablus, in the Israelioccupied West Bank Copyright AFP/File Jaafar ASHTIYEH
AI in pharma has the potential to overhaul drug discovery, clinical trials, manufacturing, and marketing by analysing vast datasets to speed up processes, reduce costs, and enable personalised medicine.
With such promise, why do so many AI/ML initiatives in biopharma fail? A new white paper indicates that it is not simply about the algorithms; instead, a more nuanced approach is required. This involves data preparation, rather than model sophistication, is the defining factor in AI success across drug development
BullFrog AI Founder and CEO Vin Singh tells that the hidden prerequisite for reliable AI in life sciences is data harmonization. In the paper, “Data Harmonization: The Hidden Prerequisite for Reliable AI/ML in Life Sciences,” BullFrog outlines where modern AI pipelines break down and how noisy, fragmented, documentheavy biomedical data often produces insights that reflect data artifacts rather than biology.
The paper also details a practical framework for fixing this problem, including:
- Engineering clinically meaningful derived features
- Creating reliable categorical variables and harmonized schemas
- Transforming unstructured clinical documents into analysisready datasets
The paper indicates that biopharma teams can reduce trial failure rates by trusting their inputs before trusting their models and how biopharma organizations to convert noisy, documentheavy data into standardized, AIready datasets in the form of harmonized, clinically contextualized formats.
The transition to this format provides 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,” says Singh.
He adds: “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 TM addresses all this by harmonizing and standardizing raw data into clean, analysisready datasets so that teams can comfortably trust their inputs.”
The white paper goes on to outline where modern AI pipelines break in life sciences and presents a practical harmonization framework built on three pillars: (1) engineering clinically meaningful derived features, (2) producing reliable categorical variables and harmonized schemas, and (3) transforming unstructured clinical documents into analysisready tables.
“The true value of AI and machine learning (ML) becomes tangible and repeatable with the harmonization of data.” Singh concludes.
