Clinical LLM Startup Guide to Training Data
Your clinical LLM needs real clinical text. Web data doesn't cut it.
General-purpose LLMs trained on web text produce plausible-sounding but clinically wrong outputs. Fixing this requires fine-tuning on real clinical documentation — discharge summaries, radiology reports, operative notes, clinic letters. This data exists in every hospital in the world. Almost none of it is accessible for commercial AI training under current data governance models.
How Rapha Protocol enables clinical LLM fine-tuning
Your base model is loaded onto the hospital's edge appliance. LoRA adapter fine-tuning runs locally against real clinical text. Only the LoRA adapter weights — typically a few megabytes — leave the institution. The raw clinical text never moves. The hospital earns 70% of the training fee.
"We're building a discharge summary generator. MIMIC-III was useful for prototyping but the text is 15 years old and from one US ICU. Real NHS discharge summaries from 2026 look completely different — different abbreviations, different structure, different medications. We needed access to actual current data. Rapha's edge model was the only way our target trust would even consider it. The key insight: our governance team cares about data movement, not data use. If the data stays in the trust, they're open to the conversation."
Clinical LLM use cases that work with compute-to-data
- Clinical note summarization — Fine-tune on real discharge summaries to generate concise handover notes.
- Radiology report generation — Train on paired chest X-rays and corresponding reports.
- Medical coding — Fine-tune on real clinical documentation with ICD-10/SNOMED codes.
- Clinical trial matching — Train NLP models on real eligibility criteria and patient records.
- Patient-facing symptom triage — Fine-tune on real clinical encounter data for more accurate triage.
LoRA adapters are not automatically privacy-preserving. Additional leakage testing, differential privacy, and minimum cohort thresholds should be evaluated. Compute-to-data eliminates data export risk, not all privacy risks.