Large language models write elegant prose, but medicine demands grounded reasoning, citations, and empathy. Our medical Q&A assistant adapts open-source LLM checkpoints to a tightly curated clinical corpus so that cardiology follow-ups, dermatology checklists, and general triage questions receive responses that feel both conversational and responsible.
What we built
- Domain-tuned knowledge. Instead of dumping every PubMed abstract into the model, we aligned it with guideline summaries, structured symptom ontologies, and real de-identified physician notes so answers mirror how doctors reason.
- Safety harnesses. Every output flows through fact-checkers that compare claims against the latest protocols, and sensitive slots (e.g., medications, dosages) trigger templated double confirmation.
- Deployment paths. The assistant can live inside patient portals or medical education sandboxes, offering both layperson language and clinician-deep dives depending on the user role.
Why it was hard
Two forces pulled in opposite directions: rich medical vocabulary pushes model size up, while on-prem deployments need lean checkpoints. Keeping PHI out of the training mix meant aggressive anonymization, plus we layered zero-trust access into every data pipeline so no intern can accidentally peek at raw charts.
Where it lands
- Online consultation. Patients type their symptoms, get contextual follow-up questions, and walk away with better-prepared notes for their doctor visit.
- Medical classrooms. Residents quiz the assistant for differential diagnosis refreshers and immediately see citations to the underlying literature.
What’s next
We are pairing the model with structured vitals streams so it can surface red flags like rising heart rates mid-chat, and we are co-designing bilingual prompts for clinics that serve multilingual communities.
