Stop reading medical jargon. Synthure translates complex clinical notes into clear, personal narratives. Understand your diagnosis. Own your health.
From clinical notes to patient understanding in seconds.
Patient is discharged. Doctor writes clinical summary with diagnosis, medications, and care instructions in medical language. This goes into Synthure.
Our AI reads the clinical note, identifies key concepts (diagnosis, medications, risks, lifestyle changes), and understands what matters most for this specific patient.
Real-time fact-checking against clinical guidelines, drug interactions, and FDA labels. Flagged items go to physician review. Nothing reaches the patient without verification.
Convert medical jargon into language patients actually understand. "ACE inhibitor for systolic dysfunction" becomes "This medication helps your heart pump more efficiently."
For each medication: when to take it, what to expect, which side effects are normal vs. warning signs that need urgent care. Not just informationβguidance.
Patient receives one-page summary they can read in 5 minutes and understand completely. Share with family. Show your doctor. Keep for reference. Own your health.
Clinical-grade AI that understands medicine, respects patient safety, and validates every claim.
Our base model is trained on 900GB of EHR data from 60K+ real patient records. It learns medical concepts, relationships between diagnoses/medications, and how to read clinical narratives like a physician would.
Transforms medical language into first-person patient language. "Systolic heart failure, EF 35%, on GDMT" becomes "Your heart isn't pumping as strongly as it should. Medications help it work more efficiently." Technically: autoregressive language modeling with attention to clinical accuracy.
Every medical claim is verified against UpToDate, clinical guidelines, and FDA databases in real-time. If something is uncertain or contradicts evidence, it's flagged for physician review before reaching the patient. This is what reduces hallucination from 92% to 12%.
We use Reinforcement Learning from Human Feedback with reward models trained on physician preferences. This teaches the AI what makes a translation clinically sound, medically safe, and patient-friendly. 50K clinical Q&A pairs guide the process.
Sub-2 second inference on GPU clusters using vLLM, quantization, and custom CUDA kernels. Fast enough that clinicians use it during patient encounters, not just for discharge summaries. Built for real clinical workflow.
Trained on HIPAA-compliant synthetic data with differential privacy guarantees (Ξ΅=0.7). De-identified throughout. Never stores patient identifiers. HIPAA-safe for hospital deployment.
Production-grade infrastructure: 900GB EHR data from 60K+ MIMIC-IV records, CUDA-optimized GPU inference, RLHF post-training with 88% hallucination reduction.
Architecture: SQL data extraction β Python batch serialization β CUDA kernel optimization β vLLM inference acceleration. Result: Sub-2s latency with 88% hallucination reduction and clinical expert validation.
Deep dives into clinical AI, patient outcomes, and healthcare innovation.
Why we chose Reinforcement Learning from Human Feedback over Direct Preference Optimization for clinical safety. Exploring reward modeling with expert annotations and PPO convergence on 50K clinical Q&A pairs.
Read more βResults from 200+ beta patients show dramatic improvement over baseline. How plain language translation improves health literacy, medication adherence, and patient-provider trust in clinical settings.
Read more βHow we achieved <1.8s P95 latency with vLLM, custom CUDA kernels, and GPU cluster orchestration. Balancing throughput with clinical response time requirements.
Read more βFrom 92% hallucination rate to 12%. How real-time fact validation, clinical guideline integration, and expert review transformed AI safety in healthcare.
Read more βHow we generated unlimited labeled training data from MIMIC-IV while maintaining strict privacy. De-identification pipelines, synthetic data validation, and clinical realism.
Read more βResearch shows patients who understand their diagnosis have higher medication adherence, fewer ED visits, and better long-term health. How AI-powered translation bridges the literacy gap.
Read more βJoin beta testing today. Limited spots for early users who want to understand their diagnosis and take control of their care.