Pharma R&D Guide to AI Training Data
Pharma AI teams spend more time negotiating data access than building models.
Every major pharmaceutical company has an AI/ML division. Every one of these divisions is blocked on the same problem: accessing real clinical data from hospitals and imaging centres to train their models. The standard approach — sponsor a clinical study, collect prospective data, wait 2-3 years — is incompatible with the pace of AI development. By the time the data arrives, the model architecture has advanced two generations.
"We spent 18 months negotiating access to oncology imaging data from three NHS trusts. The legal costs alone exceeded 200K GBP. By the time we got access, our ML team had moved on to a different architecture. With Rapha's compute-to-data model, we were training within weeks of the initial conversation. The difference for pharma is: you're not waiting for data. You're waiting for institutional approval to run compute. That's a much faster conversation than negotiating data export."
Pharma AI use cases that fit the compute-to-data model
- Drug target identification: Train on real patient genomics and clinical outcomes to identify novel targets.
- Clinical trial patient recruitment: Train NLP models on EHR data to identify eligible patients.
- Real-world evidence generation: Train models on longitudinal patient data for comparative effectiveness research.
- Biomarker discovery: Train on paired imaging, genomics, and outcomes for predictive biomarkers.
- Drug repurposing: Train on real-world treatment patterns to identify efficacy in new indications.
Regulatory advantage: auditable training provenance
For pharma companies submitting AI/ML models to regulators (FDA, EMA, MHRA), training data provenance is increasingly scrutinised. Rapha Protocol's cryptographic proof receipts provide auditable evidence of: which dataset was used, which model was trained, how many records were processed, and that raw PHI was not exported. This audit trail is specifically designed to support regulatory submissions.